Offload of storage node scale-out management to a smart network interface controller

ABSTRACT

Examples described herein relate to a network interface that includes an initiator device to determine a storage node associated with an access command based on an association between an address in the command and a storage node. The network interface can include a redirector to update the association based on messages from one or more remote storage nodes. The association can be based on a look-up table associating a namespace identifier with prefix string and object size. In some examples, the access command is compatible with NVMe over Fabrics. The initiator device can determine a remote direct memory access (RDMA) queue-pair (QP) lookup for use to perform the access command.

RELATED APPLICATION

The present application claims benefit of priority of U.S. ProvisionalPatent Application Ser. No. 62/878,742, filed Jul. 25, 2019, entitled“OFFLOAD OF STORAGE NODE SCALE-OUT MANAGEMENT TO A SMART NETWORKINTERFACE CONTROLLER” and incorporates the contents of that applicationin its entirety.

The present application is a continuation-in-part of U.S. patentapplication Ser. No. 16/023,025, filed Jun. 29, 2018, entitled“TECHNOLOGIES FOR PROVIDING ADAPTIVE DATA ACCESS REQUEST ROUTING IN ADISTRIBUTED STORAGE SYSTEM,” inventors Peterson and Sen (attorney docketnumber AA7175-US) and the present application is also acontinuation-in-part of U.S. patent application Ser. No. 16/109,606,filed Aug. 22, 2018, entitled “DISTRIBUTED STORAGE LOCATION HINTING FORNON-VOLATILE MEMORIES,” inventors Peterson et al. (attorney docketnumber AB1632-US), and claim the benefit of priority of bothapplications and incorporate both applications in their entirety.

DESCRIPTION

Distributed block storage systems provide block device functionality toapplications by presenting logical block devices that are stored insegments scattered across a large pool of remote storage devices. To usethese logical block devices, applications need to determine the locationof all the segments they need to access. Querying a directory servicefor the segment location before each I/O request greatly increasesaccess latency. Determining them all in advance places unacceptableoverhead on the system keeping that location information up to date.Popular large-scale distributed storage systems like Ceph (available atwww.ceph.com) and Gluster (available at docs.gluster.org) use consistenthashing to minimize the cost of determining logical block device segmentlocations on demand. Unfortunately, these hashing techniques cannot beused throughout large scale distributed storage in datacenters. Neitherof these are standard storage protocols, and both require specificsoftware in the client device to enable access to this storage. Theclient device software has a significant runtime and operational cost.These techniques also require the client device using the storage tohave access to the storage cluster. Some client devices are untrusted,so that form of access presents an unacceptable security risk.

The problem of performance overhead in the client device is exacerbatedwhen the client device runs on a limited resourced location such as asmart network interface card (NIC) or offloaded device. Data centers canbe required to deploy large numbers of gateway machines to enableapplications running on client systems to use the distributed blockstorage service. This adds to latency and inefficient use of networkresources because of the extra hops that are required to get to theactual data node.

One approach is to use native distributed storage client devices. Anystorage node can run a block device client. Virtual machines (VMs) canbe isolated from this via distributed block gateways integrated into thehypervisor. For “bare metal” computing systems or containers there aresome kernel implementations, but often a user mode gateway is required.These local gateways are not lightweight and require the clusteradministrator to trust the node that runs them.

Another approach is to use dedicated storage gateways. Isolation foruntrusted and bare metal applications can be accomplished by using alarge number of dedicated gateways (e.g., those using Internet SmallComputer Systems Interface (iSCSI)). These appear to the applicationlike a traditional storage array. They must collectively provide highavailability, multipath I/O (MPIO), and load balancing just like atraditional storage array. However, this adds one network hop for allstorage operations (i.e., initiator to gateway, and gateway to cluster),thereby decreasing system efficiency.

Yet another approach is to use distributed clients in a smart NIC. Thisincludes a storage client like Ceph Reliable Autonomic DistributedObject Store (RADOS) block device (RBD) in the smart NIC and present theRBD volume to the bare metal host, container, or VM as a standardhardware block (e.g., a non-volatile memory express (NVMe) devicesupporting the Non-Volatile Memory Express (NVMe) Specification,revision 1.3c, published on May 24, 2018 (“NVMe specification”) or laterrevisions). This solves application connectivity issues by using NVMe asa common protocol but requires a complex NIC implementation. NICs withenough processing cores to perform this processing may consume too muchof the power and cooling budget of a compute host housing the NIC to anunacceptable degree. The Ceph client code is fairly complex, and besttreated as a package that can be updated with the rest of the Cephcluster. When embedded as a NIC offload, that may become difficult ascluster administrators or tenants can't necessarily be trusted to managesoftware embedded in the NIC that enforces isolation of tenants fromeach other and the datacenter management network.

Non-volatile Memory Express over Fabrics (NVMe-oF™) compatible devicesprovide high performance NVMe® drives to remote systems, providing a lowlatency, efficient and high-performance solution for storage overnetwork. However, NVMe-oF F™ has a very limited deployment in cloudenvironments where multitudes of storage nodes are available for use,because of lack of a scale-out solution in a highly distributed storageenvironment.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a simplified diagram of at least one embodiment of a datacenter for executing workloads with disaggregated resources.

FIG. 2 is a simplified diagram of at least one embodiment of a pod thatmay be included in a data center.

FIG. 3 is a perspective view of at least one embodiment of a rack thatmay be included in a pod.

FIG. 4 is a side elevation view of a rack.

FIG. 5 is a perspective view of a rack having a sled mounted therein.

FIG. 6 is a simplified block diagram of at least one embodiment of a topside of a sled.

FIG. 7 is a simplified block diagram of at least one embodiment of abottom side of a sled.

FIG. 8 is a simplified block diagram of at least one embodiment of acompute sled.

FIG. 9 is a top perspective view of at least one embodiment of a computesled.

FIG. 10 is a simplified block diagram of at least one embodiment of anaccelerator sled usable in a data center.

FIG. 11 is a top perspective view of at least one embodiment of anaccelerator sled.

FIG. 12 is a simplified block diagram of at least one embodiment of astorage sled usable in a data center.

FIG. 13 is a top perspective view of at least one embodiment of astorage sled.

FIG. 14 is a simplified block diagram of at least one embodiment of amemory sled usable in a data center.

FIG. 15 depicts a system for executing one or more workloads.

FIG. 16 depicts a system with distributed self-learning NVMe-oFcompatible devices, using an abstracted view of an NVMe-oF initiator.

FIG. 17 depicts a process flow for access of a remote storage node usingNVMe-oF.

FIG. 18 depicts an example system.

FIG. 19A depicts an example system.

FIG. 19B depicts an example system.

FIG. 20 depicts a process to perform hint processing and translate hintsfor a target node access.

FIG. 21 depicts a process to process hashing hints.

FIG. 22 depicts a system.

FIG. 23 depicts an example network interface.

DETAILED DESCRIPTION

Various embodiments provide distributed storage location hinting in acomputing system supporting NVMe over an interconnect fabric. NVMe overan interconnect fabric is described in the NVMe Over Fabric (NVMe-oF)Specification, revision 1.1, published in June 2016 (available atnvmexpress.org), and variations and revisions thereof, the entirety ofwhich is incorporated herein by reference. Embodiments include changesto NVMe-oF to improve the delivery of location hints. These mechanismscan be used to handle failover of replicated NVMe-of targets, andstriping across multiple NVMe-oF targets. The hinting mechanism ofembodiments include consistent hashing compatible with distributedstorage systems such as Ceph and Gluster (Ceph and Gluster arereferenced herein as examples and in other implementations otherdistributed storage systems may be used). This consistent hash form ofalgorithmic location hints allows client devices to determine on theirown which storage node should contain each region of the logical blockdevice and to send the I/O request directly to that storage node. Thesimple form of location hinting mechanism is used to handle thetemporary situations where objects in a storage cluster are not yetwhere they should be.

Embodiments provide logical block devices (consistent with Ceph andGluster, for example) to be presented as hardware NVMe devices to VMs,containers, or “bare metal” compute nodes with complete isolation fromthe distributed storage system. A smart NIC runs only a slightlymodified NVMe-oF initiator and will require fewer processing cores thana completely embedded RBD client. Without smart NIC support in thecompute node, a NVMe-oF initiator (implemented in a Linux™ kernel, forexample) can be modified in the same way and provide the samecapabilities, and still provide most of the same isolation of the baremetal nodes from the storage cluster.

Storage servers can benefit from a NIC offload technique similar to theone client devices use to shield them from frequently forwarded I/Orequests. This can enable unmodified NVMe-oF initiators to accesslogical volumes without concentrating their gateway workload on a singlestorage node processor. The addition of the consistent hashing mechanismenables storage initiators to determine the correct location most of thetime for a logical block device region. When a logical block device isassembled from tens or hundreds of 1 gigabyte (GB) or larger allocationunits by a distributed volume manager (DVM), the cost of sending I/O toa storage node that has to forward it and send back a location hint isseldomly incurred. A distributed storage system (such as Ceph andGluster, for example), will assemble their logical block devices frommany much smaller segments. In embodiments, the locations of where thesesegments should be can be easily determined. Using the consistenthashing hint approach of embodiments of the present invention,forwarding and hinting will only happen while a region is not locatedwhere it should be, and only for initiators that do not have the currentlocation hint.

Various embodiments include a storage subsystem; a non-volatile memoryexpress over fabric (NVMe-oF) interconnect; and a host system coupled tothe storage subsystem over the NVMe-oF interconnect, the host system toobtain one or more location hints applicable to a range of logical blockaddresses of a received input/output (I/O) request for the storagesubsystem; for each logical block address in the I/O request: apply amost specific location hint of the one or more location hints thatmatches that logical block address to identify a destination in thestorage subsystem for the I/O request; when the most specific locationhint is a consistent hash hint, process the consistent hash hint;forward the I/O request to the destination and return a completionstatus for the I/O request; when a location hint log page has changed,process the location hint log page; and when any location hint refers toNVMe-oF qualified names not included in the immediately preceding queryby the discovery service, process the immediately preceding query again.

Various embodiments provide a compute device comprising a redirectordevice to receive, from an initiator device, a request that identifies adata set to be accessed; determine, from a set of routing rulesindicative of target devices associated with data sets, whether theidentified data set is available in a storage server associated with thepresent redirector device; forward, in response to a determination thatthe identified data set is not available in a storage server associatedwith the present redirector device, the request to a target deviceassociated with the data set in the routing rules; and send, to theinitiator device, an identification of the target device associated withthe data set in the routing rules.

According to some embodiments, the redirector device is further toreceive, from the target device, an identification of a different targetdevice to which data requests associated with the identified data setare to be sent; and store the identification of the different targetdevice in the routing rules.

According to some embodiments, the redirector device is further to send,to the initiator device, the identification of the different targetdevice.

According to some embodiments, the redirector device is further toreceive, from a manager server, default routing rules indicative ofpredefined target devices to which data access requests are to be sent.

According to some embodiments, to determine, from a set of routing rulesindicative of target devices associated with data sets, whether theidentified data set is available in a storage server associated with thepresent redirector device further comprises to select a routing rulefrom a plurality of routing rules as a function of a specificity of eachrouting rule associated with the identified data set.

According to some embodiments, to receive, from an initiator device, arequest that identifies a data set comprises to receive the request fromanother redirector device.

According to some embodiments, wherein the redirector device is furtherto receive data that indicates that a data set that was previouslylocated at a storage server associated with the present redirectordevice has moved to a different storage server.

According to some embodiments, wherein the redirector device is furtherto receive data that indicates that a data set that was previouslylocated at one storage server has been moved to a second storage server,wherein the second storage server is associated with the presentredirector device.

According to some embodiments, wherein to determine, from a set ofrouting rules indicative of target devices associated with data sets,whether the identified data set is available in a storage serverassociated with the present redirector device further comprises to matcha compute server that initiated the request with one of multiple targetdevices identified in the routing rules.

According to some embodiments, wherein to receive, from an initiatordevice, a request that identifies a data set to be accessed comprises toreceive a request to write to the identified data set; and theredirector device is further to forward the request to multiple targetdevices associated with replicas of the data set.

According to some embodiments, wherein to receive a request thatidentifies a data set to be accessed comprises to receive a request toaccess a logical block address.

According to some embodiments, wherein to receive a request thatidentifies a data set to be accessed comprises to receive a request toaccess an extent of a volume.

FIG. 1 depicts a data center in which disaggregated resources maycooperatively execute one or more workloads (e.g., applications onbehalf of customers) includes multiple pods 110, 120, 130, 140, a podbeing or including one or more rows of racks. Of course, although datacenter 100 is shown with multiple pods, in some embodiments, the datacenter 100 may be embodied as a single pod. As described in more detailherein, each rack houses multiple sleds, each of which may be primarilyequipped with a particular type of resource (e.g., memory devices, datastorage devices, accelerator devices, general purpose processors), i.e.,resources that can be logically coupled to form a composed node, whichcan act as, for example, a server. In the illustrative embodiment, thesleds in each pod 110, 120, 130, 140 are connected to multiple podswitches (e.g., switches that route data communications to and fromsleds within the pod). The pod switches, in turn, connect with spineswitches 150 that switch communications among pods (e.g., the pods 110,120, 130, 140) in the data center 100. In some embodiments, the sledsmay be connected with a fabric using Intel Omni-Path technology. Inother embodiments, the sleds may be connected with other fabrics, suchas InfiniB and or Ethernet. As described in more detail herein,resources within sleds in the data center 100 may be allocated to agroup (referred to herein as a “managed node”) containing resources fromone or more sleds to be collectively utilized in the execution of aworkload. The workload can execute as if the resources belonging to themanaged node were located on the same sled. The resources in a managednode may belong to sleds belonging to different racks, and even todifferent pods 110, 120, 130, 140. As such, some resources of a singlesled may be allocated to one managed node while other resources of thesame sled are allocated to a different managed node (e.g., one processorassigned to one managed node and another processor of the same sledassigned to a different managed node).

A data center comprising disaggregated resources, such as data center100, can be used in a wide variety of contexts, such as enterprise,government, cloud service provider, and communications service provider(e.g., Telco's), as well in a wide variety of sizes, from cloud serviceprovider mega-data centers that consume over 100,000 sq. ft. to single-or multi-rack installations for use in base stations.

The disaggregation of resources to sleds comprised predominantly of asingle type of resource (e.g., compute sleds comprising primarilycompute resources, memory sleds containing primarily memory resources),and the selective allocation and deallocation of the disaggregatedresources to form a managed node assigned to execute a workload improvesthe operation and resource usage of the data center 100 relative totypical data centers comprised of hyperconverged servers containingcompute, memory, storage and perhaps additional resources in a singlechassis. For example, because sleds predominantly contain resources of aparticular type, resources of a given type can be upgraded independentlyof other resources. Additionally, because different resources types(processors, storage, accelerators, etc.) typically have differentrefresh rates, greater resource utilization and reduced total cost ofownership may be achieved. For example, a data center operator canupgrade the processors throughout their facility by only swapping outthe compute sleds. In such a case, accelerator and storage resources maynot be contemporaneously upgraded and, rather, may be allowed tocontinue operating until those resources are scheduled for their ownrefresh. Resource utilization may also increase. For example, if managednodes are composed based on requirements of the workloads that will berunning on them, resources within a node are more likely to be fullyutilized. Such utilization may allow for more managed nodes to run in adata center with a given set of resources, or for a data center expectedto run a given set of workloads, to be built using fewer resources.

FIG. 2 depicts a pod. A pod can include a set of rows 200, 210, 220, 230of racks 240. Each rack 240 may house multiple sleds (e.g., sixteensleds) and provide power and data connections to the housed sleds, asdescribed in more detail herein. In the illustrative embodiment, theracks in each row 200, 210, 220, 230 are connected to multiple podswitches 250, 260. The pod switch 250 includes a set of ports 252 towhich the sleds of the racks of the pod 110 are connected and anotherset of ports 254 that connect the pod 110 to the spine switches 150 toprovide connectivity to other pods in the data center 100. Similarly,the pod switch 260 includes a set of ports 262 to which the sleds of theracks of the pod 110 are connected and a set of ports 264 that connectthe pod 110 to the spine switches 150. As such, the use of the pair ofswitches 250, 260 provides an amount of redundancy to the pod 110. Forexample, if either of the switches 250, 260 fails, the sleds in the pod110 may still maintain data communication with the remainder of the datacenter 100 (e.g., sleds of other pods) through the other switch 250,260. Furthermore, in the illustrative embodiment, the switches 150, 250,260 may be embodied as dual-mode optical switches, capable of routingboth Ethernet protocol communications carrying Internet Protocol (IP)packets and communications according to a second, high-performancelink-layer protocol (e.g., PCI Express) via optical signaling media ofan optical fabric.

It should be appreciated that each of the other pods 120, 130, 140 (aswell as any additional pods of the data center 100) may be similarlystructured as, and have components similar to, the pod 110 shown in anddescribed in regard to FIG. 2 (e.g., each pod may have rows of rackshousing multiple sleds as described above). Additionally, while two podswitches 250, 260 are shown, it should be understood that in otherembodiments, each pod 110, 120, 130, 140 may be connected to a differentnumber of pod switches, providing even more failover capacity. Ofcourse, in other embodiments, pods may be arranged differently than therows-of-racks configuration shown in FIGS. 1-2. For example, a pod maybe embodied as multiple sets of racks in which each set of racks isarranged radially, i.e., the racks are equidistant from a center switch.

Referring now to FIGS. 3-5, each illustrative rack 240 of the datacenter 100 includes two elongated support posts 302, 304, which arearranged vertically. For example, the elongated support posts 302, 304may extend upwardly from a floor of the data center 100 when deployed.The rack 240 also includes one or more horizontal pairs 310 of elongatedsupport arms 312 (identified in FIG. 3 via a dashed ellipse) configuredto support a sled of the data center 100 as discussed below. Oneelongated support arm 312 of the pair of elongated support arms 312extends outwardly from the elongated support post 302 and the otherelongated support arm 312 extends outwardly from the elongated supportpost 304.

In the illustrative embodiments, each sled of the data center 100 isembodied as a chassis-less sled. That is, each sled has a chassis-lesscircuit board substrate on which physical resources (e.g., processors,memory, accelerators, storage, etc.) are mounted as discussed in moredetail below. As such, the rack 240 is configured to receive thechassis-less sleds. For example, each pair 310 of elongated support arms312 defines a sled slot 320 of the rack 240, which is configured toreceive a corresponding chassis-less sled. To do so, each illustrativeelongated support arm 312 includes a circuit board guide 330 configuredto receive the chassis-less circuit board substrate of the sled. Eachcircuit board guide 330 is secured to, or otherwise mounted to, a topside 332 of the corresponding elongated support arm 312. For example, inthe illustrative embodiment, each circuit board guide 330 is mounted ata distal end of the corresponding elongated support arm 312 relative tothe corresponding elongated support post 302, 304. For clarity of theFigures, not every circuit board guide 330 may be referenced in eachFigure.

Each circuit board guide 330 includes an inner wall that defines acircuit board slot 380 configured to receive the chassis-less circuitboard substrate of a sled 400 when the sled 400 is received in thecorresponding sled slot 320 of the rack 240. To do so, as shown in FIG.4, a user (or robot) aligns the chassis-less circuit board substrate ofan illustrative chassis-less sled 400 to a sled slot 320. The user, orrobot, may then slide the chassis-less circuit board substrate forwardinto the sled slot 320 such that each side edge 414 of the chassis-lesscircuit board substrate is received in a corresponding circuit boardslot 380 of the circuit board guides 330 of the pair 310 of elongatedsupport arms 312 that define the corresponding sled slot 320 as shown inFIG. 4. By having robotically accessible and robotically manipulatablesleds comprising disaggregated resources, each type of resource can beupgraded independently of each other and at their own optimized refreshrate. Furthermore, the sleds are configured to blindly mate with powerand data communication cables in each rack 240, enhancing their abilityto be quickly removed, upgraded, reinstalled, and/or replaced. As such,in some embodiments, the data center 100 may operate (e.g., executeworkloads, undergo maintenance and/or upgrades, etc.) without humaninvolvement on the data center floor. In other embodiments, a human mayfacilitate one or more maintenance or upgrade operations in the datacenter 100.

It should be appreciated that each circuit board guide 330 is dualsided. That is, each circuit board guide 330 includes an inner wall thatdefines a circuit board slot 380 on each side of the circuit board guide330. In this way, each circuit board guide 330 can support achassis-less circuit board substrate on either side. As such, a singleadditional elongated support post may be added to the rack 240 to turnthe rack 240 into a two-rack solution that can hold twice as many sledslots 320 as shown in FIG. 3. The illustrative rack 240 includes sevenpairs 310 of elongated support arms 312 that define a correspondingseven sled slots 320, each configured to receive and support acorresponding sled 400 as discussed above. Of course, in otherembodiments, the rack 240 may include additional or fewer pairs 310 ofelongated support arms 312 (i.e., additional or fewer sled slots 320).It should be appreciated that because the sled 400 is chassis-less, thesled 400 may have an overall height that is different than typicalservers. As such, in some embodiments, the height of each sled slot 320may be shorter than the height of a typical server (e.g., shorter than asingle rank unit, “1U”). That is, the vertical distance between eachpair 310 of elongated support arms 312 may be less than a standard rackunit “1U.” Additionally, due to the relative decrease in height of thesled slots 320, the overall height of the rack 240 in some embodimentsmay be shorter than the height of traditional rack enclosures. Forexample, in some embodiments, each of the elongated support posts 302,304 may have a length of six feet or less. Again, in other embodiments,the rack 240 may have different dimensions. For example, in someembodiments, the vertical distance between each pair 310 of elongatedsupport arms 312 may be greater than a standard rack until “1U”. In suchembodiments, the increased vertical distance between the sleds allowsfor larger heat sinks to be attached to the physical resources and forlarger fans to be used (e.g., in the fan array 370 described below) forcooling each sled, which in turn can allow the physical resources tooperate at increased power levels. Further, it should be appreciatedthat the rack 240 does not include any walls, enclosures, or the like.Rather, the rack 240 is an enclosure-less rack that is opened to thelocal environment. Of course, in some cases, an end plate may beattached to one of the elongated support posts 302, 304 in thosesituations in which the rack 240 forms an end-of-row rack in the datacenter 100.

In some embodiments, various interconnects may be routed upwardly ordownwardly through the elongated support posts 302, 304. To facilitatesuch routing, each elongated support post 302, 304 includes an innerwall that defines an inner chamber in which interconnects may belocated. The interconnects routed through the elongated support posts302, 304 may be embodied as any type of interconnects including, but notlimited to, data or communication interconnects to provide communicationconnections to each sled slot 320, power interconnects to provide powerto each sled slot 320, and/or other types of interconnects.

The rack 240, in the illustrative embodiment, includes a supportplatform on which a corresponding optical data connector (not shown) ismounted. Each optical data connector is associated with a correspondingsled slot 320 and is configured to mate with an optical data connectorof a corresponding sled 400 when the sled 400 is received in thecorresponding sled slot 320. In some embodiments, optical connectionsbetween components (e.g., sleds, racks, and switches) in the data center100 are made with a blind mate optical connection. For example, a dooron each cable may prevent dust from contaminating the fiber inside thecable. In the process of connecting to a blind mate optical connectormechanism, the door is pushed open when the end of the cable approachesor enters the connector mechanism. Subsequently, the optical fiberinside the cable may enter a gel within the connector mechanism and theoptical fiber of one cable comes into contact with the optical fiber ofanother cable within the gel inside the connector mechanism.

The illustrative rack 240 also includes a fan array 370 coupled to thecross-support arms of the rack 240. The fan array 370 includes one ormore rows of cooling fans 372, which are aligned in a horizontal linebetween the elongated support posts 302, 304. In the illustrativeembodiment, the fan array 370 includes a row of cooling fans 372 foreach sled slot 320 of the rack 240. As discussed above, each sled 400does not include any on-board cooling system in the illustrativeembodiment and, as such, the fan array 370 provides cooling for eachsled 400 received in the rack 240. Each rack 240, in the illustrativeembodiment, also includes a power supply associated with each sled slot320. Each power supply is secured to one of the elongated support arms312 of the pair 310 of elongated support arms 312 that define thecorresponding sled slot 320. For example, the rack 240 may include apower supply coupled or secured to each elongated support arm 312extending from the elongated support post 302. Each power supplyincludes a power connector configured to mate with a power connector ofthe sled 400 when the sled 400 is received in the corresponding sledslot 320. In the illustrative embodiment, the sled 400 does not includeany on-board power supply and, as such, the power supplies provided inthe rack 240 supply power to corresponding sleds 400 when mounted to therack 240. Each power supply is configured to satisfy the powerrequirements for its associated sled, which can vary from sled to sled.Additionally, the power supplies provided in the rack 240 can operateindependent of each other. That is, within a single rack, a first powersupply providing power to a compute sled can provide power levels thatare different than power levels supplied by a second power supplyproviding power to an accelerator sled. The power supplies may becontrollable at the sled level or rack level, and may be controlledlocally by components on the associated sled or remotely, such as byanother sled or an orchestrator.

Referring now to FIG. 6, the sled 400, in the illustrative embodiment,is configured to be mounted in a corresponding rack 240 of the datacenter 100 as discussed above. In some embodiments, each sled 400 may beoptimized or otherwise configured for performing particular tasks, suchas compute tasks, acceleration tasks, data storage tasks, etc. Forexample, the sled 400 may be embodied as a compute sled 800 as discussedbelow in regard to FIGS. 8-9, an accelerator sled 1000 as discussedbelow in regard to FIGS. 10-11, a storage sled 1200 as discussed belowin regard to FIGS. 12-13, or as a sled optimized or otherwise configuredto perform other specialized tasks, such as a memory sled 1400,discussed below in regard to FIG. 14.

As discussed above, the illustrative sled 400 includes a chassis-lesscircuit board substrate 602, which supports various physical resources(e.g., electrical components) mounted thereon. It should be appreciatedthat the circuit board substrate 602 is “chassis-less” in that the sled400 does not include a housing or enclosure. Rather, the chassis-lesscircuit board substrate 602 is open to the local environment. Thechassis-less circuit board substrate 602 may be formed from any materialcapable of supporting the various electrical components mounted thereon.For example, in an illustrative embodiment, the chassis-less circuitboard substrate 602 is formed from an FR-4 glass-reinforced epoxylaminate material. Of course, other materials may be used to form thechassis-less circuit board substrate 602 in other embodiments.

As discussed in more detail below, the chassis-less circuit boardsubstrate 602 includes multiple features that improve the thermalcooling characteristics of the various electrical components mounted onthe chassis-less circuit board substrate 602. As discussed, thechassis-less circuit board substrate 602 does not include a housing orenclosure, which may improve the airflow over the electrical componentsof the sled 400 by reducing those structures that may inhibit air flow.For example, because the chassis-less circuit board substrate 602 is notpositioned in an individual housing or enclosure, there is novertically-arranged backplane (e.g., a backplate of the chassis)attached to the chassis-less circuit board substrate 602, which couldinhibit air flow across the electrical components. Additionally, thechassis-less circuit board substrate 602 has a geometric shapeconfigured to reduce the length of the airflow path across theelectrical components mounted to the chassis-less circuit boardsubstrate 602. For example, the illustrative chassis-less circuit boardsubstrate 602 has a width 604 that is greater than a depth 606 of thechassis-less circuit board substrate 602. In one particular embodiment,for example, the chassis-less circuit board substrate 602 has a width ofabout 21 inches and a depth of about 9 inches, compared to a typicalserver that has a width of about 17 inches and a depth of about 39inches. As such, an airflow path 608 that extends from a front edge 610of the chassis-less circuit board substrate 602 toward a rear edge 612has a shorter distance relative to typical servers, which may improvethe thermal cooling characteristics of the sled 400. Furthermore,although not illustrated in FIG. 6, the various physical resourcesmounted to the chassis-less circuit board substrate 602 are mounted incorresponding locations such that no two substantively heat-producingelectrical components shadow each other as discussed in more detailbelow. That is, no two electrical components, which produce appreciableheat during operation (i.e., greater than a nominal heat sufficientenough to adversely impact the cooling of another electrical component),are mounted to the chassis-less circuit board substrate 602 linearlyin-line with each other along the direction of the airflow path 608(i.e., along a direction extending from the front edge 610 toward therear edge 612 of the chassis-less circuit board substrate 602).

As discussed above, the illustrative sled 400 includes one or morephysical resources 620 mounted to a top side 650 of the chassis-lesscircuit board substrate 602. Although two physical resources 620 areshown in FIG. 6, it should be appreciated that the sled 400 may includeone, two, or more physical resources 620 in other embodiments. Thephysical resources 620 may be embodied as any type of processor,controller, or other compute circuit capable of performing various taskssuch as compute functions and/or controlling the functions of the sled400 depending on, for example, the type or intended functionality of thesled 400. For example, as discussed in more detail below, the physicalresources 620 may be embodied as high-performance processors inembodiments in which the sled 400 is embodied as a compute sled, asaccelerator co-processors or circuits in embodiments in which the sled400 is embodied as an accelerator sled, storage controllers inembodiments in which the sled 400 is embodied as a storage sled, or aset of memory devices in embodiments in which the sled 400 is embodiedas a memory sled.

The sled 400 also includes one or more additional physical resources 630mounted to the top side 650 of the chassis-less circuit board substrate602. In the illustrative embodiment, the additional physical resourcesinclude a network interface controller (NIC) as discussed in more detailbelow. Of course, depending on the type and functionality of the sled400, the physical resources 630 may include additional or otherelectrical components, circuits, and/or devices in other embodiments.

The physical resources 620 are communicatively coupled to the physicalresources 630 via an input/output (I/O) subsystem 622. The I/O subsystem622 may be embodied as circuitry and/or components to facilitateinput/output operations with the physical resources 620, the physicalresources 630, and/or other components of the sled 400. For example, theI/O subsystem 622 may be embodied as, or otherwise include, memorycontroller hubs, input/output control hubs, integrated sensor hubs,firmware devices, communication links (e.g., point-to-point links, buslinks, wires, cables, waveguides, light guides, printed circuit boardtraces, etc.), and/or other components and subsystems to facilitate theinput/output operations. In the illustrative embodiment, the I/Osubsystem 622 is embodied as, or otherwise includes, a double data rate4 (DDR4) data bus or a DDR5 data bus.

In some embodiments, the sled 400 may also include aresource-to-resource interconnect 624. The resource-to-resourceinterconnect 624 may be embodied as any type of communicationinterconnect capable of facilitating resource-to-resourcecommunications. In the illustrative embodiment, the resource-to-resourceinterconnect 624 is embodied as a high-speed point-to-point interconnect(e.g., faster than the I/O subsystem 622). For example, theresource-to-resource interconnect 624 may be embodied as a QuickPathInterconnect (QPI), an UltraPath Interconnect (UPI), PCI express (PCIe),or other high-speed point-to-point interconnect dedicated toresource-to-resource communications.

The sled 400 also includes a power connector 640 configured to mate witha corresponding power connector of the rack 240 when the sled 400 ismounted in the corresponding rack 240. The sled 400 receives power froma power supply of the rack 240 via the power connector 640 to supplypower to the various electrical components of the sled 400. That is, thesled 400 does not include any local power supply (i.e., an on-boardpower supply) to provide power to the electrical components of the sled400. The exclusion of a local or on-board power supply facilitates thereduction in the overall footprint of the chassis-less circuit boardsubstrate 602, which may increase the thermal cooling characteristics ofthe various electrical components mounted on the chassis-less circuitboard substrate 602 as discussed above. In some embodiments, voltageregulators are placed on a bottom side 750 (see FIG. 7) of thechassis-less circuit board substrate 602 directly opposite of theprocessors 820 (see FIG. 8), and power is routed from the voltageregulators to the processors 820 by vias extending through the circuitboard substrate 602. Such a configuration provides an increased thermalbudget, additional current and/or voltage, and better voltage controlrelative to typical printed circuit boards in which processor power isdelivered from a voltage regulator, in part, by printed circuit traces.

In some embodiments, the sled 400 may also include mounting features 642configured to mate with a mounting arm, or other structure, of a robotto facilitate the placement of the sled 600 in a rack 240 by the robot.The mounting features 642 may be embodied as any type of physicalstructures that allow the robot to grasp the sled 400 without damagingthe chassis-less circuit board substrate 602 or the electricalcomponents mounted thereto. For example, in some embodiments, themounting features 642 may be embodied as non-conductive pads attached tothe chassis-less circuit board substrate 602. In other embodiments, themounting features may be embodied as brackets, braces, or other similarstructures attached to the chassis-less circuit board substrate 602. Theparticular number, shape, size, and/or make-up of the mounting feature642 may depend on the design of the robot configured to manage the sled400.

Referring now to FIG. 7, in addition to the physical resources 630mounted on the top side 650 of the chassis-less circuit board substrate602, the sled 400 also includes one or more memory devices 720 mountedto a bottom side 750 of the chassis-less circuit board substrate 602.That is, the chassis-less circuit board substrate 602 is embodied as adouble-sided circuit board. The physical resources 620 arecommunicatively coupled to the memory devices 720 via the I/O subsystem622. For example, the physical resources 620 and the memory devices 720may be communicatively coupled by one or more vias extending through thechassis-less circuit board substrate 602. Each physical resource 620 maybe communicatively coupled to a different set of one or more memorydevices 720 in some embodiments. Alternatively, in other embodiments,each physical resource 620 may be communicatively coupled to each memorydevice 720.

The memory devices 720 may be embodied as any type of memory devicecapable of storing data for the physical resources 620 during operationof the sled 400, such as any type of volatile (e.g., dynamic randomaccess memory (DRAM), etc.) or non-volatile memory. Volatile memory maybe a storage medium that requires power to maintain the state of datastored by the medium. Non-limiting examples of volatile memory mayinclude various types of random access memory (RAM), such as dynamicrandom access memory (DRAM) or static random access memory (SRAM). Oneparticular type of DRAM that may be used in a memory module issynchronous dynamic random access memory (SDRAM). In particularembodiments, DRAM of a memory component may comply with a standardpromulgated by JEDEC, such as JESD79F for DDR SDRAM, JESD79-2F for DDR2SDRAM, JESD79-3F for DDR3 SDRAM, JESD79-4A for DDR4 SDRAM, JESD209 forLow Power DDR (LPDDR), JESD209-2 for LPDDR2, JESD209-3 for LPDDR3, andJESD209-4 for LPDDR4. Such standards (and similar standards) may bereferred to as DDR-based standards and communication interfaces of thestorage devices that implement such standards may be referred to asDDR-based interfaces.

In one embodiment, the memory device is a block addressable memorydevice, such as those based on NAND or NOR technologies. A block can beany size such as but not limited to 2 KB, 4 KB, 8 KB, and so forth. Amemory device may also include next-generation nonvolatile devices, suchas Intel Optane® memory or other byte addressable write-in-placenonvolatile memory devices. In one embodiment, the memory device may beor may include memory devices that use chalcogenide glass,multi-threshold level NAND flash memory, NOR flash memory, single ormulti-level Phase Change Memory (PCM), a resistive memory, nanowirememory, ferroelectric transistor random access memory (FeTRAM),anti-ferroelectric memory, magnetoresistive random access memory (MRAM)memory that incorporates memristor technology, resistive memoryincluding the metal oxide base, the oxygen vacancy base and theconductive bridge Random Access Memory (CB-RAM), or spin transfer torque(STT)-MRAM, a spintronic magnetic junction memory based device, amagnetic tunneling junction (MTJ) based device, a DW (Domain Wall) andSOT (Spin Orbit Transfer) based device, a thyristor based memory device,or a combination of any of the above, or other memory. The memory devicemay refer to the die itself and/or to a packaged memory product. In someembodiments, the memory device may comprise a transistor-less stackablecross point architecture in which memory cells sit at the intersectionof word lines and bit lines and are individually addressable and inwhich bit storage is based on a change in bulk resistance.

Referring now to FIG. 8, in some embodiments, the sled 400 may beembodied as a compute sled 800. The compute sled 800 is optimized, orotherwise configured, to perform compute tasks. Of course, as discussedabove, the compute sled 800 may rely on other sleds, such asacceleration sleds and/or storage sleds, to perform such compute tasks.The compute sled 800 includes various physical resources (e.g.,electrical components) similar to the physical resources of the sled400, which have been identified in FIG. 8 using the same referencenumbers. The description of such components provided above in regard toFIGS. 6 and 7 applies to the corresponding components of the computesled 800 and is not repeated herein for clarity of the description ofthe compute sled 800.

In the illustrative compute sled 800, the physical resources 620 areembodied as processors 820. Although only two processors 820 are shownin FIG. 8, it should be appreciated that the compute sled 800 mayinclude additional processors 820 in other embodiments. Illustratively,the processors 820 are embodied as high-performance processors 820 andmay be configured to operate at a relatively high power rating. Althoughthe processors 820 generate additional heat operating at power ratingsgreater than typical processors (which operate at around 155-230 W), theenhanced thermal cooling characteristics of the chassis-less circuitboard substrate 602 discussed above facilitate the higher poweroperation. For example, in the illustrative embodiment, the processors820 are configured to operate at a power rating of at least 250 W. Insome embodiments, the processors 820 may be configured to operate at apower rating of at least 350 W.

In some embodiments, the compute sled 800 may also include aprocessor-to-processor interconnect 842. Similar to theresource-to-resource interconnect 624 of the sled 400 discussed above,the processor-to-processor interconnect 842 may be embodied as any typeof communication interconnect capable of facilitatingprocessor-to-processor interconnect 842 communications. In theillustrative embodiment, the processor-to-processor interconnect 842 isembodied as a high-speed point-to-point interconnect (e.g., faster thanthe I/O subsystem 622). For example, the processor-to-processorinterconnect 842 may be embodied as a QuickPath Interconnect (QPI), anUltraPath Interconnect (UPI), or other high-speed point-to-pointinterconnect dedicated to processor-to-processor communications (e.g.,PCIe).

The compute sled 800 also includes a communication circuit 830. Theillustrative communication circuit 830 includes a network interfacecontroller (NIC) 832, which may also be referred to as a host fabricinterface (HFI). The NIC 832 may be embodied as, or otherwise include,any type of integrated circuit, discrete circuits, controller chips,chipsets, add-in-boards, daughtercards, network interface cards, orother devices that may be used by the compute sled 800 to connect withanother compute device (e.g., with other sleds 400). In someembodiments, the NIC 832 may be embodied as part of a system-on-a-chip(SoC) that includes one or more processors, or included on a multichippackage that also contains one or more processors. In some embodiments,the NIC 832 may include a local processor (not shown) and/or a localmemory (not shown) that are both local to the NIC 832. In suchembodiments, the local processor of the NIC 832 may be capable ofperforming one or more of the functions of the processors 820.Additionally or alternatively, in such embodiments, the local memory ofthe NIC 832 may be integrated into one or more components of the computesled at the board level, socket level, chip level, and/or other levels.

The communication circuit 830 is communicatively coupled to an opticaldata connector 834. The optical data connector 834 is configured to matewith a corresponding optical data connector of the rack 240 when thecompute sled 800 is mounted in the rack 240. Illustratively, the opticaldata connector 834 includes a plurality of optical fibers which leadfrom a mating surface of the optical data connector 834 to an opticaltransceiver 836. The optical transceiver 836 is configured to convertincoming optical signals from the rack-side optical data connector toelectrical signals and to convert electrical signals to outgoing opticalsignals to the rack-side optical data connector. Although shown asforming part of the optical data connector 834 in the illustrativeembodiment, the optical transceiver 836 may form a portion of thecommunication circuit 830 in other embodiments.

In some embodiments, the compute sled 800 may also include an expansionconnector 840. In such embodiments, the expansion connector 840 isconfigured to mate with a corresponding connector of an expansionchassis-less circuit board substrate to provide additional physicalresources to the compute sled 800. The additional physical resources maybe used, for example, by the processors 820 during operation of thecompute sled 800. The expansion chassis-less circuit board substrate maybe substantially similar to the chassis-less circuit board substrate 602discussed above and may include various electrical components mountedthereto. The particular electrical components mounted to the expansionchassis-less circuit board substrate may depend on the intendedfunctionality of the expansion chassis-less circuit board substrate. Forexample, the expansion chassis-less circuit board substrate may provideadditional compute resources, memory resources, and/or storageresources. As such, the additional physical resources of the expansionchassis-less circuit board substrate may include, but is not limited to,processors, memory devices, storage devices, and/or accelerator circuitsincluding, for example, field programmable gate arrays (FPGA),application-specific integrated circuits (ASICs), securityco-processors, graphics processing units (GPUs), machine learningcircuits, or other specialized processors, controllers, devices, and/orcircuits.

Referring now to FIG. 9, an illustrative embodiment of the compute sled800 is shown. As shown, the processors 820, communication circuit 830,and optical data connector 834 are mounted to the top side 650 of thechassis-less circuit board substrate 602. Any suitable attachment ormounting technology may be used to mount the physical resources of thecompute sled 800 to the chassis-less circuit board substrate 602. Forexample, the various physical resources may be mounted in correspondingsockets (e.g., a processor socket), holders, or brackets. In some cases,some of the electrical components may be directly mounted to thechassis-less circuit board substrate 602 via soldering or similartechniques.

As discussed above, the individual processors 820 and communicationcircuit 830 are mounted to the top side 650 of the chassis-less circuitboard substrate 602 such that no two heat-producing, electricalcomponents shadow each other. In the illustrative embodiment, theprocessors 820 and communication circuit 830 are mounted incorresponding locations on the top side 650 of the chassis-less circuitboard substrate 602 such that no two of those physical resources arelinearly in-line with others along the direction of the airflow path608. It should be appreciated that, although the optical data connector834 is in-line with the communication circuit 830, the optical dataconnector 834 produces no or nominal heat during operation.

The memory devices 720 of the compute sled 800 are mounted to the bottomside 750 of the of the chassis-less circuit board substrate 602 asdiscussed above in regard to the sled 400. Although mounted to thebottom side 750, the memory devices 720 are communicatively coupled tothe processors 820 located on the top side 650 via the I/O subsystem622. Because the chassis-less circuit board substrate 602 is embodied asa double-sided circuit board, the memory devices 720 and the processors820 may be communicatively coupled by one or more vias, connectors, orother mechanisms extending through the chassis-less circuit boardsubstrate 602. Of course, each processor 820 may be communicativelycoupled to a different set of one or more memory devices 720 in someembodiments. Alternatively, in other embodiments, each processor 820 maybe communicatively coupled to each memory device 720. In someembodiments, the memory devices 720 may be mounted to one or more memorymezzanines on the bottom side of the chassis-less circuit boardsubstrate 602 and may interconnect with a corresponding processor 820through a ball-grid array.

Each of the processors 820 includes a heatsink 850 secured thereto. Dueto the mounting of the memory devices 720 to the bottom side 750 of thechassis-less circuit board substrate 602 (as well as the verticalspacing of the sleds 400 in the corresponding rack 240), the top side650 of the chassis-less circuit board substrate 602 includes additional“free” area or space that facilitates the use of heatsinks 850 having alarger size relative to traditional heatsinks used in typical servers.Additionally, due to the improved thermal cooling characteristics of thechassis-less circuit board substrate 602, none of the processorheatsinks 850 include cooling fans attached thereto. That is, each ofthe heatsinks 850 is embodied as a fan-less heatsink. In someembodiments, the heat sinks 850 mounted atop the processors 820 mayoverlap with the heat sink attached to the communication circuit 830 inthe direction of the airflow path 608 due to their increased size, asillustratively suggested by FIG. 9.

Referring now to FIG. 10, in some embodiments, the sled 400 may beembodied as an accelerator sled 1000. The accelerator sled 1000 isconfigured, to perform specialized compute tasks, such as machinelearning, encryption, hashing, or other computational-intensive task. Insome embodiments, for example, a compute sled 800 may offload tasks tothe accelerator sled 1000 during operation. The accelerator sled 1000includes various components similar to components of the sled 400 and/orcompute sled 800, which have been identified in FIG. 10 using the samereference numbers. The description of such components provided above inregard to FIGS. 6, 7, and 8 apply to the corresponding components of theaccelerator sled 1000 and is not repeated herein for clarity of thedescription of the accelerator sled 1000.

In the illustrative accelerator sled 1000, the physical resources 620are embodied as accelerator circuits 1020. Although only two acceleratorcircuits 1020 are shown in FIG. 10, it should be appreciated that theaccelerator sled 1000 may include additional accelerator circuits 1020in other embodiments. For example, as shown in FIG. 11, the acceleratorsled 1000 may include four accelerator circuits 1020 in someembodiments. The accelerator circuits 1020 may be embodied as any typeof processor, co-processor, compute circuit, or other device capable ofperforming compute or processing operations. For example, theaccelerator circuits 1020 may be embodied as, for example, centralprocessing units, cores, field programmable gate arrays (FPGA),application-specific integrated circuits (ASICs), programmable controllogic (PCL), security co-processors, graphics processing units (GPUs),neuromorphic processor units, quantum computers, machine learningcircuits, or other specialized processors, controllers, devices, and/orcircuits.

In some embodiments, the accelerator sled 1000 may also include anaccelerator-to-accelerator interconnect 1042. Similar to theresource-to-resource interconnect 624 of the sled 600 discussed above,the accelerator-to-accelerator interconnect 1042 may be embodied as anytype of communication interconnect capable of facilitatingaccelerator-to-accelerator communications. In the illustrativeembodiment, the accelerator-to-accelerator interconnect 1042 is embodiedas a high-speed point-to-point interconnect (e.g., faster than the I/Osubsystem 622). For example, the accelerator-to-accelerator interconnect1042 may be embodied as a QuickPath Interconnect (QPI), an UltraPathInterconnect (UPI), or other high-speed point-to-point interconnectdedicated to processor-to-processor communications. In some embodiments,the accelerator circuits 1020 may be daisy-chained with a primaryaccelerator circuit 1020 connected to the NIC 832 and memory 720 throughthe I/O subsystem 622 and a secondary accelerator circuit 1020 connectedto the NIC 832 and memory 720 through a primary accelerator circuit1020.

Referring now to FIG. 11, an illustrative embodiment of the acceleratorsled 1000 is shown. As discussed above, the accelerator circuits 1020,communication circuit 830, and optical data connector 834 are mounted tothe top side 650 of the chassis-less circuit board substrate 602. Again,the individual accelerator circuits 1020 and communication circuit 830are mounted to the top side 650 of the chassis-less circuit boardsubstrate 602 such that no two heat-producing, electrical componentsshadow each other as discussed above. The memory devices 720 of theaccelerator sled 1000 are mounted to the bottom side 750 of the of thechassis-less circuit board substrate 602 as discussed above in regard tothe sled 600. Although mounted to the bottom side 750, the memorydevices 720 are communicatively coupled to the accelerator circuits 1020located on the top side 650 via the I/O subsystem 622 (e.g., throughvias). Further, each of the accelerator circuits 1020 may include aheatsink 1070 that is larger than a traditional heatsink used in aserver. As discussed above with reference to the heatsinks 870, theheatsinks 1070 may be larger than traditional heatsinks because of the“free” area provided by the memory resources 720 being located on thebottom side 750 of the chassis-less circuit board substrate 602 ratherthan on the top side 650.

Referring now to FIG. 12, in some embodiments, the sled 400 may beembodied as a storage sled 1200. The storage sled 1200 is configured, tostore data in a data storage 1250 local to the storage sled 1200. Forexample, during operation, a compute sled 800 or an accelerator sled1000 may store and retrieve data from the data storage 1250 of thestorage sled 1200. The storage sled 1200 includes various componentssimilar to components of the sled 400 and/or the compute sled 800, whichhave been identified in FIG. 12 using the same reference numbers. Thedescription of such components provided above with regard to FIGS. 6, 7,and 8 apply to the corresponding components of the storage sled 1200 andis not repeated herein for clarity of the description of the storagesled 1200.

In the illustrative storage sled 1200, the physical resources 620 areembodied as storage controllers 1220. Although only two storagecontrollers 1220 are shown in FIG. 12, it should be appreciated that thestorage sled 1200 may include additional storage controllers 1220 inother embodiments. The storage controllers 1220 may be embodied as anytype of processor, controller, or control circuit capable of controllingthe storage and retrieval of data into the data storage 1250 based onrequests received via the communication circuit 830. In the illustrativeembodiment, the storage controllers 1220 are embodied as relativelylow-power processors or controllers. For example, in some embodiments,the storage controllers 1220 may be configured to operate at a powerrating of about 75 watts.

In some embodiments, the storage sled 1200 may also include acontroller-to-controller interconnect 1242. Similar to theresource-to-resource interconnect 624 of the sled 400 discussed above,the controller-to-controller interconnect 1242 may be embodied as anytype of communication interconnect capable of facilitatingcontroller-to-controller communications. In the illustrative embodiment,the controller-to-controller interconnect 1242 is embodied as ahigh-speed point-to-point interconnect (e.g., faster than the I/Osubsystem 622). For example, the controller-to-controller interconnect1242 may be embodied as a QuickPath Interconnect (QPI), an UltraPathInterconnect (UPI), or other high-speed point-to-point interconnectdedicated to processor-to-processor communications.

Referring now to FIG. 13, an illustrative embodiment of the storage sled1200 is shown. In the illustrative embodiment, the data storage 1250 isembodied as, or otherwise includes, a storage cage 1252 configured tohouse one or more solid state drives (SSDs) 1254. To do so, the storagecage 1252 includes a number of mounting slots 1256, each of which isconfigured to receive a corresponding solid state drive 1254. Each ofthe mounting slots 1256 includes a number of drive guides 1258 thatcooperate to define an access opening 1260 of the corresponding mountingslot 1256. The storage cage 1252 is secured to the chassis-less circuitboard substrate 602 such that the access openings face away from (i.e.,toward the front of) the chassis-less circuit board substrate 602. Assuch, solid state drives 1254 are accessible while the storage sled 1200is mounted in a corresponding rack 204. For example, a solid state drive1254 may be swapped out of a rack 240 (e.g., via a robot) while thestorage sled 1200 remains mounted in the corresponding rack 240.

The storage cage 1252 illustratively includes sixteen mounting slots1256 and is capable of mounting and storing sixteen solid state drives1254. Of course, the storage cage 1252 may be configured to storeadditional or fewer solid state drives 1254 in other embodiments.Additionally, in the illustrative embodiment, the solid state driversare mounted vertically in the storage cage 1252, but may be mounted inthe storage cage 1252 in a different orientation in other embodiments.Each solid state drive 1254 may be embodied as any type of data storagedevice capable of storing long term data. To do so, the solid statedrives 1254 may include volatile and non-volatile memory devicesdiscussed above.

As shown in FIG. 13, the storage controllers 1220, the communicationcircuit 830, and the optical data connector 834 are illustrativelymounted to the top side 650 of the chassis-less circuit board substrate602. Again, as discussed above, any suitable attachment or mountingtechnology may be used to mount the electrical components of the storagesled 1200 to the chassis-less circuit board substrate 602 including, forexample, sockets (e.g., a processor socket), holders, brackets, solderedconnections, and/or other mounting or securing techniques.

As discussed above, the individual storage controllers 1220 and thecommunication circuit 830 are mounted to the top side 650 of thechassis-less circuit board substrate 602 such that no twoheat-producing, electrical components shadow each other. For example,the storage controllers 1220 and the communication circuit 830 aremounted in corresponding locations on the top side 650 of thechassis-less circuit board substrate 602 such that no two of thoseelectrical components are linearly in-line with each other along thedirection of the airflow path 608.

The memory devices 720 of the storage sled 1200 are mounted to thebottom side 750 of the of the chassis-less circuit board substrate 602as discussed above in regard to the sled 400. Although mounted to thebottom side 750, the memory devices 720 are communicatively coupled tothe storage controllers 1220 located on the top side 650 via the I/Osubsystem 622. Again, because the chassis-less circuit board substrate602 is embodied as a double-sided circuit board, the memory devices 720and the storage controllers 1220 may be communicatively coupled by oneor more vias, connectors, or other mechanisms extending through thechassis-less circuit board substrate 602. Each of the storagecontrollers 1220 includes a heatsink 1270 secured thereto. As discussedabove, due to the improved thermal cooling characteristics of thechassis-less circuit board substrate 602 of the storage sled 1200, noneof the heatsinks 1270 include cooling fans attached thereto. That is,each of the heatsinks 1270 is embodied as a fan-less heatsink.

Referring now to FIG. 14, in some embodiments, the sled 400 may beembodied as a memory sled 1400. The storage sled 1400 is optimized, orotherwise configured, to provide other sleds 400 (e.g., compute sleds800, accelerator sleds 1000, etc.) with access to a pool of memory(e.g., in two or more sets 1430, 1432 of memory devices 720) local tothe memory sled 1200. For example, during operation, a compute sled 800or an accelerator sled 1000 may remotely write to and/or read from oneor more of the memory sets 1430, 1432 of the memory sled 1200 using alogical address space that maps to physical addresses in the memory sets1430, 1432. The memory sled 1400 includes various components similar tocomponents of the sled 400 and/or the compute sled 800, which have beenidentified in FIG. 14 using the same reference numbers. The descriptionof such components provided above in regard to FIGS. 6, 7, and 8 applyto the corresponding components of the memory sled 1400 and is notrepeated herein for clarity of the description of the memory sled 1400.

In the illustrative memory sled 1400, the physical resources 620 areembodied as memory controllers 1420. Although only two memorycontrollers 1420 are shown in FIG. 14, it should be appreciated that thememory sled 1400 may include additional memory controllers 1420 in otherembodiments. The memory controllers 1420 may be embodied as any type ofprocessor, controller, or control circuit capable of controlling thewriting and reading of data into the memory sets 1430, 1432 based onrequests received via the communication circuit 830. In the illustrativeembodiment, each memory controller 1420 is connected to a correspondingmemory set 1430, 1432 to write to and read from memory devices 720within the corresponding memory set 1430, 1432 and enforce anypermissions (e.g., read, write, etc.) associated with sled 400 that hassent a request to the memory sled 1400 to perform a memory accessoperation (e.g., read or write).

In some embodiments, the memory sled 1400 may also include acontroller-to-controller interconnect 1442. Similar to theresource-to-resource interconnect 624 of the sled 400 discussed above,the controller-to-controller interconnect 1442 may be embodied as anytype of communication interconnect capable of facilitatingcontroller-to-controller communications. In the illustrative embodiment,the controller-to-controller interconnect 1442 is embodied as ahigh-speed point-to-point interconnect (e.g., faster than the I/Osubsystem 622). For example, the controller-to-controller interconnect1442 may be embodied as a QuickPath Interconnect (QPI), an UltraPathInterconnect (UPI), or other high-speed point-to-point interconnectdedicated to processor-to-processor communications. As such, in someembodiments, a memory controller 1420 may access, through thecontroller-to-controller interconnect 1442, memory that is within thememory set 1432 associated with another memory controller 1420. In someembodiments, a scalable memory controller is made of multiple smallermemory controllers, referred to herein as “chiplets”, on a memory sled(e.g., the memory sled 1400). The chiplets may be interconnected (e.g.,using EMIB (Embedded Multi-Die Interconnect Bridge)). The combinedchiplet memory controller may scale up to a relatively large number ofmemory controllers and I/O ports, (e.g., up to 16 memory channels). Insome embodiments, the memory controllers 1420 may implement a memoryinterleave (e.g., one memory address is mapped to the memory set 1430,the next memory address is mapped to the memory set 1432, and the thirdaddress is mapped to the memory set 1430, etc.). The interleaving may bemanaged within the memory controllers 1420, or from CPU sockets (e.g.,of the compute sled 800) across network links to the memory sets 1430,1432, and may improve the latency associated with performing memoryaccess operations as compared to accessing contiguous memory addressesfrom the same memory device.

Further, in some embodiments, the memory sled 1400 may be connected toone or more other sleds 400 (e.g., in the same rack 240 or an adjacentrack 240) through a waveguide, using the waveguide connector 1480. Inthe illustrative embodiment, the waveguides are 64 millimeter waveguidesthat provide 16 Rx (i.e., receive) lanes and 16 Tx (i.e., transmit)lanes. Each lane, in the illustrative embodiment, is either 16 GHz or 32GHz. In other embodiments, the frequencies may be different. Using awaveguide may provide high throughput access to the memory pool (e.g.,the memory sets 1430, 1432) to another sled (e.g., a sled 400 in thesame rack 240 or an adjacent rack 240 as the memory sled 1400) withoutadding to the load on the optical data connector 834.

Referring now to FIG. 15, a system for executing one or more workloads(e.g., applications) may be implemented in accordance with the datacenter 100. In the illustrative embodiment, the system 1510 includes anorchestrator server 1520, which may be embodied as a managed nodecomprising a compute device (e.g., a processor 820 on a compute sled800) executing management software (e.g., a cloud operating environment,such as OpenStack) that is communicatively coupled to multiple sleds 400including a large number of compute sleds 1530 (e.g., each similar tothe compute sled 800), memory sleds 1540 (e.g., each similar to thememory sled 1400), accelerator sleds 1550 (e.g., each similar to thememory sled 1000), and storage sleds 1560 (e.g., each similar to thestorage sled 1200). One or more of the sleds 1530, 1540, 1550, 1560 maybe grouped into a managed node 1570, such as by the orchestrator server1520, to collectively perform a workload (e.g., an application 1532executed in a virtual machine or in a container). The managed node 1570may be embodied as an assembly of physical resources 620, such asprocessors 820, memory resources 720, accelerator circuits 1020, or datastorage 1250, from the same or different sleds 400. Further, the managednode may be established, defined, or “spun up” by the orchestratorserver 1520 at the time a workload is to be assigned to the managed nodeor at any other time, and may exist regardless of whether any workloadsare presently assigned to the managed node. In the illustrativeembodiment, the orchestrator server 1520 may selectively allocate and/ordeallocate physical resources 620 from the sleds 400 and/or add orremove one or more sleds 400 from the managed node 1570 as a function ofquality of service (QoS) targets (e.g., a target throughput, a targetlatency, a target number instructions per second, etc.) associated witha service level agreement for the workload (e.g., the application 1532).In doing so, the orchestrator server 1520 may receive telemetry dataindicative of performance conditions (e.g., throughput, latency,instructions per second, etc.) in each sled 400 of the managed node 1570and compare the telemetry data to the quality of service targets todetermine whether the quality of service targets are being satisfied.The orchestrator server 1520 may additionally determine whether one ormore physical resources may be deallocated from the managed node 1570while still satisfying the QoS targets, thereby freeing up thosephysical resources for use in another managed node (e.g., to execute adifferent workload). Alternatively, if the QoS targets are not presentlysatisfied, the orchestrator server 1520 may determine to dynamicallyallocate additional physical resources to assist in the execution of theworkload (e.g., the application 1532) while the workload is executing.Similarly, the orchestrator server 1520 may determine to dynamicallydeallocate physical resources from a managed node if the orchestratorserver 1520 determines that deallocating the physical resource wouldresult in QoS targets still being met.

Additionally, in some embodiments, the orchestrator server 1520 mayidentify trends in the resource utilization of the workload (e.g., theapplication 1532), such as by identifying phases of execution (e.g.,time periods in which different operations, each having differentresource utilizations characteristics, are performed) of the workload(e.g., the application 1532) and pre-emptively identifying availableresources in the data center 100 and allocating them to the managed node1570 (e.g., within a predefined time period of the associated phasebeginning). In some embodiments, the orchestrator server 1520 may modelperformance based on various latencies and a distribution scheme toplace workloads among compute sleds and other resources (e.g.,accelerator sleds, memory sleds, storage sleds) in the data center 100.For example, the orchestrator server 1520 may utilize a model thataccounts for the performance of resources on the sleds 400 (e.g., FPGAperformance, memory access latency, etc.) and the performance (e.g.,congestion, latency, bandwidth) of the path through the network to theresource (e.g., FPGA). As such, the orchestrator server 1520 maydetermine which resource(s) should be used with which workloads based onthe total latency associated with each potential resource available inthe data center 100 (e.g., the latency associated with the performanceof the resource itself in addition to the latency associated with thepath through the network between the compute sled executing the workloadand the sled 400 on which the resource is located).

In some embodiments, the orchestrator server 1520 may generate a map ofheat generation in the data center 100 using telemetry data (e.g.,temperatures, fan speeds, etc.) reported from the sleds 400 and allocateresources to managed nodes as a function of the map of heat generationand predicted heat generation associated with different workloads, tomaintain a target temperature and heat distribution in the data center100. Additionally or alternatively, in some embodiments, theorchestrator server 1520 may organize received telemetry data into ahierarchical model that is indicative of a relationship between themanaged nodes (e.g., a spatial relationship such as the physicallocations of the resources of the managed nodes within the data center100 and/or a functional relationship, such as groupings of the managednodes by the customers the managed nodes provide services for, the typesof functions typically performed by the managed nodes, managed nodesthat typically share or exchange workloads among each other, etc.).Based on differences in the physical locations and resources in themanaged nodes, a given workload may exhibit different resourceutilizations (e.g., cause a different internal temperature, use adifferent percentage of processor or memory capacity) across theresources of different managed nodes. The orchestrator server 1520 maydetermine the differences based on the telemetry data stored in thehierarchical model and factor the differences into a prediction offuture resource utilization of a workload if the workload is reassignedfrom one managed node to another managed node, to accurately balanceresource utilization in the data center 100. In some embodiments, theorchestrator server 1520 may identify patterns in resource utilizationphases of the workloads and use the patterns to predict future resourceutilization of the workloads.

To reduce the computational load on the orchestrator server 1520 and thedata transfer load on the network, in some embodiments, the orchestratorserver 1520 may send self-test information to the sleds 400 to enableeach sled 400 to locally (e.g., on the sled 400) determine whethertelemetry data generated by the sled 400 satisfies one or moreconditions (e.g., an available capacity that satisfies a predefinedthreshold, a temperature that satisfies a predefined threshold, etc.).Each sled 400 may then report back a simplified result (e.g., yes or no)to the orchestrator server 1520, which the orchestrator server 1520 mayutilize in determining the allocation of resources to managed nodes.

FIG. 16 depicts a system with distributed learning of NVMe-oF compatibledevices, using an abstracted view of an NVMe-oF initiator. Host H-1accesses LN-A (logical NVMe namespace A). The DVM has concatenated four1 GB extents of three different physical NVMe namespaces (PNs) to form 4GB logical namespace LN-A. The extents are: 0-1 GB of PN-A in subsystemS-1, 2-3 GB of PN-B in subsystem S-2, 1-2 GB of PN-C in subsystem S-3,and 2-3G of PN-A in subsystem S-1.

This is a general distributed volume manager (DVM) use case, where LNsare allocated fluidly from free regions of PNs. Here, the PN extents areall the same size. A DVM is free to allocate extents of any size. Whenadjacent extents of the LN are on the same subsystem, a single locationhint spanning their LBA range can be used.

In this example, host H-1 contacts the discovery service (DS), retrievesthe list of subsystems, and connects to subsystems S-1, S-2, and S-3.Host H-1 discovers that these are all redirectors, and all provide LN-A.At that point, H-1 has three default targets for all IO to all LBAs ofLN-A. All targets are equally preferred because so far there have beenno location hints expressing any priority of the alternatives. In thiscase, they all refer to both reads and writes, and they all refer to thevarious 1 GB extents of LN-A.

In this example, each subsystem could send H-1 initial hints about theextents it owns. Host H-1 would then have a complete map of LN-A atinitialization, and may never send an IO that gets forwarded. However,in some cases, this approach may not be scaled beyond a hundred extents.

The DVM is responsible for maintaining the mapping of LN-A to itscomponent PN extents, and for initializing the mappers in each subsystemwith entries for the extents it owns. The DVM also initializes themappers in each subsystem so IO to any LBA can be completed by any ofthem by populating each mapper with a complete map of all 4 extents ofLN-A.

FIG. 17 depicts a process flow for access of a remote storage node usingNVMe-oF. An approach provides a redirector and hints about which remotestorage server a block should be read/written from/to. A host sendsfirst input output (TO) (e.g., read or write) and then learns whereother IOs should go. The host (compute node 1.2) learns completeknowledge of where a block is stored within a few IOs. The hostcomputing system executes a NVMe-oF initiator software that finds one ormore NVMe-oF subsystems and connects to all of them to find an NVMenamespace. Namespaces are large global unique identifiers. The initiatorreceives hints to know where ranges are located. A log page can be usedto identify locations of devices that store content associated withlogical addresses.

An approach provides a redirector device to receive, from an initiatordevice, a request that identifies a data set to be accessed. Theredirector device is also to determine, from a set of routing rulesindicative of target devices associated with data sets, whether theidentified data set is available in a storage server (e.g., SSD)associated with the present redirector device, forward, in response to adetermination that the identified data set is not available in a storageserver associated with the present redirector device, the request to atarget device associated with the data set in the routing rules, andsend, to the initiator device, an identification of the target deviceassociated with the data set in the routing rules.

Various embodiments provide for offloading of storage scale-outmanagement to a network interface card (NIC) or SmartNIC with an NVMe-oFinitiator. The NVMe-oF initiator can be implemented in hardware and/orsoftware by the NIC. Various embodiments reduce central processing unit(CPU) utilization of an integrated system on chip (SoC) in a NIC andprovide a scale-out solution for the NVMe-oF initiator in a NIC in cloudor fabric environments. By contrast, a scale-out solution based onNVMe-oF software-implemented initiator in a host computing platform canintroduce higher CPU utilization because of the overheads of hashingalgorithms and hint processing and can introduce additional latencybecause of software-based flows. Scale-out can refer to addition ofadditional resources (e.g., compute, memory, or storage) based onaccessible addition of resources to a network or fabric instead ofincreasing capacity of existing resources. For a smart NIC with hardwarebased NVMe-oF initiator, for bare metal hosting usages, offload of thehashing algorithms and hints processing to the processing pipelines canbe beneficial because the integrated SoC (in SmartNlC) has a limitedcompute capacity. A bare metal server can be a physical server deviceallocated for use to a single tenant or customer. The tenant canconfigure the server according to its needs for performance, securityand reliability. An alternative to a bare metal server is a hypervisorserver, where multiple users share a virtual server's compute, storageand other resources. However, embodiments can be used for hypervisorservers or non-bare metal use cases.

In some embodiments, a NIC can take a variety of parameters and hints(e.g., messages) and then use lookup tables to find the remote storagesystem for each storage block (LBA) address. The control plane softwareexecuting on the integrated SoC of the NIC can, at run-time, update thelookup tables and parameters/hints to the scale-out scheme. Variousembodiments provide software assisted translation of hashing hints intoa hint table lookup and provides flexibility of adjusting input sets tothe hashing algorithm in different deployment scenarios. Variousembodiments also reduce latency and/or jitter (e.g., variation inresponse), which are important metrics for storage applications. Notethat reference to storage can also refer to volatile memory andreference to memory can refer to non-volatile storage or non-volatilememory including byte-addressable memory.

On reboot, a host might erase or discard hints. Various embodimentsstore hints on smart NIC that may retain hints independent of the host.

FIG. 18 depicts an example system. The system can use offload redirector1802, NVMe-oF initiator 1804 and hint table 1810 to a NIC, Smart NIC,switch, or buffer. Redirector 1802 directs 110 requests from anapplication run by a host computing system to the correct NVMe-oF targetbased on hint table 1810. Target redirector 1822 forwards 110 to thecorrect storage node and propagates hints backward and forward, allowingfully decoupled and legacy clients. Redirector 1802 updates local hinttable based on received hints about a volume. Redirector 1802 eventuallyis updated with an NVMe-oF volume distribution across nodes by use ofhints.

The hints allow flexible mapping of logical block address (LB A) to oneor more storage nodes connected to host 1800 through a connection suchas a fabric, network, interconnect or bus. For example, simple hints,striping hints, or hashing hints (described herein) can be supported.Hints can enable a NIC to support NVMe-oF storage services ranging frompair-wise High Availability (HA) to massive scale-out. In this example,a host sends I/O1 to storage node 1820-1 but storage node 1820-1 cannotfulfill I/O1 as it does not have the associated LBA. Redirector 1822transfers I/O2 to storage node 1820-2 to fulfill the transaction.Storage node 1820-1 transmits a hint to host 1800 to identify I/O1 isstored in storage node 1820-2. Redirector 1802 updates hint table 1810with the node address of I/O2 for a starting LBA associated with theI/O2. In another transaction for an LBA identified using I/O2, the hostsends I/O2 directly to storage node 1820-2.

FIG. 19A depicts an example system. A host system 1900 using interface1902 can be connected to storage nodes, memory pools, accelerators, adistributed volume manager, and other devices using connection 1904.Interface 1902 can use various embodiments to identify an address of astorage device to which a memory access request is to be transmitted.Connection 1904 can provide communications using one or more of a bus,interconnect, fabric, or network compatible or compliant with one ormore of: Ethernet (IEEE 802.3), remote direct memory access (RDMA),InfiniB and, Internet Wide Area RDMA Protocol (iWARP), quick UDPInternet Connections (QUIC), RDMA over Converged Ethernet (RoCE),Peripheral Component Interconnect express (PCIe), Intel QuickPathInterconnect (QPI), Intel Ultra Path Interconnect (UPI), Intel On-ChipSystem Fabric (IOSF), Omnipath, Compute Express Link (CXL),HyperTransport, high-speed fabric, NVLink, Advanced Microcontroller BusArchitecture (AMBA) interconnect, OpenCAPI, Gen-Z, Cache CoherentInterconnect for Accelerators (CCIX), 3GPP Long Term Evolution (LTE)(4G), 3GPP 5G, and variations thereof. Data can be copied or stored tovirtualized storage nodes using a protocol such as NVMe over Fabrics(NVMe-oF) or NVMe.

FIG. 19B depicts an example system. Various embodiments offload I/Oredirect management into NIC 1950 (e.g., a SmartNIC) rather than hostsystem. Note that NIC and SmartNIC can be used interchangeably. Theinitiator and redirector can be offloaded into the NIC 1950 pipeline fora high-performance design and for bare-metal hosting usages.

Host system can execute an operating system (OS) 1930. OS 1930 canaccess one or more NVMe drivers using a kernel, or virtualized executionenvironment to access a VF or PF. VF can represent a virtual NVMefunction. For example, VF can use single root input/outputvirtualization (SR-IOV) or Scalable I/O Virtualization (SIOV). PF canrepresent a physical NIC function.

A virtualized execution environment can include at least a virtualmachine or a container. A virtual machine (VM) can be software that runsan operating system and one or more applications. A VM can be defined byspecification, configuration files, virtual disk file, non-volatilerandom access memory (NVRAM) setting file, and the log file and isbacked by the physical resources of a host computing platform. A VM canbe an OS or application environment that is installed on software, whichimitates dedicated hardware. The end user has the same experience on avirtual machine as they would have on dedicated hardware. Specializedsoftware, called a hypervisor, emulates the PC client or server's CPU,memory, hard disk, network and other hardware resources completely,enabling virtual machines to share the resources. The hypervisor canemulate multiple virtual hardware platforms that are isolated from eachother, allowing virtual machines to run Linux® and Windows® Serveroperating systems on the same underlying physical host.

A container can be a software package of applications, configurationsand dependencies so the applications run reliably on one computingenvironment to another. Containers can share an operating systeminstalled on the server platform and run as isolated processes. Acontainer can be a software package that contains everything thesoftware needs to run such as system tools, libraries, and settings.Containers are not installed like traditional software programs, whichallows them to be isolated from the other software and the operatingsystem itself. Isolation can include access of memory by a particularcontainer but not another container. The isolated nature of containersprovides several benefits. First, the software in a container will runthe same in different environments. For example, a container thatincludes PHP and MySQL can run identically on both a Linux computer anda Windows machine. Second, containers provide added security since thesoftware will not affect the host operating system. While an installedapplication may alter system settings and modify resources, such as theWindows registry, a container can only modify settings within thecontainer.

NIC 1950 can provide NVMe-oF initiator 1952 as available for use by ahost system. NVMe-oF initiator 1952 can be implemented in hardware andsupport scale-out system and table lookup. Lookup tables can stored inon-die static random access memory (SRAM) or implemented as a cache inSRAM backed by DDR memory. The lookup table size can be large enough tosupport many volumes and a large range of LBAs. A scale-out hint tablelookup can be implemented in any combination of hardware or software andenables a full hardware-based data flow in scale-out deployment, avoidsruntime software-based table lookup and thus reduces CPU utilization onthe SoC 1960 that is integrated into NIC 1950. A mixed hardware andsoftware implementation can reduce latency and jitters, which areimportant metrics for storage applications, especially for remote solidstate drives (SSDs) or memory devices accessed over NVMe-oF.

Redirector 1962 can be software executed by a processor. NVMe-oFinitiator 1952 can hand off I/Os to redirector 1962 in certain casessuch as, but not limited to: failure of a QP, any other failure of aforwarded I/O, LBA ranges not covered by the lookup table (e.g., becausethe table is not large enough), and I/Os that are to be fragmented(e.g., because the QP for their starting LBA is different from the QP oftheir last LBA, or because they are too large for the target QP).

Initiator 1952 can keep statistics (e.g., counts) of a number of times ahint table entry is used, so redirector 1962 can determine what to evictfrom the table.

SoC 1960 can be used to perform correction of various error or flaggedconditions such as identified QP of a command is inoperative or down, nomapping table entry for an IO. SoC 1960 can inform host system of anerror if SoC 1960 is unable to correct the error and the host system canattempt to correct the error.

An example operation of the system of FIG. 19B can be as follows. At 1,NVMe driver from the host sends an I/O command to a VF exposed by theSmartNIC. At 2, the I/O command is processed by NVMe-oF initiator 1952,which moves data into local buffers if it is a write command. For an I/Ocommand to a device coupled using a fabric or network, initiator 1952translates the NVMe command into an NVMe-oF command. At 3, initiator1952 retrieves (PF/VF number, Namespace ID, LBA number) from the I/Ocommand context. At 4, initiator 1952 applies scale-out by performing ahint table lookup to find an RDMA QP to use to send the NVMe-oF command.If there is a match in the table between the I/O command context and anentry in the table, retrieval takes place of the RDMA QP used to sendthe NVMe-oF command. If there is no match, a default RDMA QP (e.g., adefault storage node) is used to send the NVMe-oF command or the commandis sent to SoC software for processing. At 6, the target storage node1972-1 receives the IO command (I/O1). If the LB A is not in the rangeof target storage node 1972-1, the target storage node 1972-1 forwardsthe IO command to the next best target node (e.g., specified by DVM forthis target storage node) (e.g., node 1972-2), and sends the latest hint(the one it used to make the “next best target” decision) to the NIC1950.

At 7, initiator 1952 in NIC 1950 receives the latest hint from targetnode 1972-1. Redirector 1962 processes the hint, translates the hintinto a simple lookup entry, and updates the lookup table with the latesthint. At 8, for the next IO command from the same VF/NSID (namespaceidentifier), a table match will be hit in the table and an NVMe QP isretrieved from the matched table entry. NVMe-oF command (1102) is sentto the correct target node 1972-2 using the shortest network pathinstead of being sent to a storage node 1972-1, which forwards the I/Orequest, directly or indirectly, to the destination node 1972-2.

The following provides an example format of entries in a lookup tableused by initiator 1952.

LBA Range (for I/O simple hint)/ Direc- Output Hints Type Stripe size(for tion (RDMA QP (Simple, striping hint)/ (Read (R), or LogicalStriping, Object Size for Write (W) connection namespace Hashing)hashing or RW) ID) (PF0, VF1, Simple [0, 1023] RW QP1 NSID1) (PF0, VF1,Simple [1024, 2047]   RW QP2 NSID1) (PF0, VF2, Striping 1024 RW [QP3,QP4, NSID2) QP5] (PF0, VF2, Simple [0, 1023] RW QP3 NSID2) (PF0, VF3,Hash 4096 RW NA (follow NSID3) hash lookup table flow) . . . Note (PF#,VF#, SQ#, NSID#) can be replaced by pNSID, which is a unique namespaceID after remapping the NSID from the host command into an internalunique number.

NIC 1950 can use SoC 1960 to provide NVMe-OF control plane and asoftware (SW) implemented redirector 1962. Redirector 1962 running onSoC 1960's processor can process hints from remote storage nodes or adistributed volume manager and update the lookup tables of initiator1952. In some examples, redirector 1962 can support various one or moreof: simple hint, striping hint, or hashing hint. A simple hint is usedfor a small namespace and small number of QPs. A simple hint includesfields [Start LBA, Length, Read/Write, Target Extent List]. For example,LBA0-X range goes to server0 (represented as any or more of RDMA queuepair, TCP, QUIC), whereas LBA-Y-Z range goes to server1 (represented asany or more of RDMA queue pair, TCP, QUIC).

A striping hint can be used for data to be reliably distributed andprovides higher aggregated I/O operations (IOPs) across multipleservers. A striping hint can include fields [Start/End LBA, Stripe Size,Number of Extents, Target Extent List]. An LBA range can be striped overextents. A striping hint can be transformed to LBAs to stripe dataacross multiple servers. For example, LBA0 is assigned to server0 LBA1is assigned to server1, and so forth.

For example, hashing hints are described at least with respect to FIG.21. Hashing Hint can include fields [Object Size, Object Name Format,Hash Function, Hash Table Page]. Hashing hints can be used for scale-outstorage or memory nodes to expand the number of available addressablenodes.

The following provides example definitions of hints used in distributedstorage network.

Simple Hint Over the Distributed Storage Network.

Field Description LN Start LBA ‘0’ in both for “all LBAs” LN End LBA I/Odirection R, W, or both Multiple location policy One of: “Orderindicates preference”, “client RR”, “client chooses” Locations If >1location supplied, apply multiple location policy above LocationSubsystem NQN One of the subsystems identified by 1 the DS DestinationNGUID Usually the same as the LN NGUID (optional) Offset The same as‘Start LBA’ Locations 2-n Repeats for as many locations as there are

Stripe Hint Over the Distributed Storage Network

Field Description LN Start LBA Offset in LN of this stripe group LNstripe size Bytes per stripe (or stripe number bitmask) Stripe extentcount PN extents per stripe group (or extent number bitmask) Stripegroup members Array of length “Stripe extent count” Extent Subsystem NQNOne of the subsystems identified by 1 the DS Destination NGUID Usuallythe same as the LN NGUID (optional) Offset The same as ‘Start LBA’Extents 2-n Repeats for as many extents as there are

The destination NGUID can be removed from some or all hints in someembodiments. Removing the destination NGUID can save 128 bits per hintfor use cases that do not use it. The destination NGUID can be used if aNIC has attached NVMe drives, a hyperconverged storage use case, wherethe SSDs are all located in compute nodes, and the DVM is distributedacross the smart NICs. Another use case for destination NGUID ishandling Ceph RBD clones. A Ceph RBD clone can be layers of RBD volumeseach containing only the blocks that are different from their parent,and where each layer in the clone stack needs a different consistenthash hint because its objects have a different name prefix, and possiblya different PG table which may share no OSDs at all with the childbecause the child was cloned into a different OSD pool.

Hashing Hint Over the Distributed Storage Network

Field Example Description LN chunk size Bytes per object Chunk nameformat string Leading and trailing portion of object name, and formatspecifier for the chunk number. Hash function Code for one of thepre-defined hash functions Hash bucket count Size of location tableindexed by hash Hash table log page Log page containing the locationtable

Various embodiments discussed herein can be combined with softwareassisted processing of a hashing hint. Hash-based look up of storagenodes can be used for cloud storage systems such as Ceph or Gluster.SoC-executed software (e.g., redirector 1962) can process the hashinghint by performing a hash calculation, translating the hashing hint intoa simple hint (e.g., LBA range to RDMA QP) and programming the lookuptable used by initiator 1952. In some examples, redirector 1962 orinitiator 1952 can include or use a hashing engine to offload thehashing calculation. Initiator 1952 can perform inline hashingprocessing and use the hash value to lookup an RDMA queue pair (QP)number to avoid software processing of an NVMe-oF command. Of course,any combination of hardware and/or software can be used by allembodiments described herein.

In some examples, initiator 1952 can use hardware to implement an R.Jenkins compatible hash engine 1954 and processor-executed software toprogram prefix string for a namespace (pre-str) and object size. Otherhash schemes can be used such as Toeplitz hash, XOR calculation, SHA256,and so forth. The system can use hardware and/or software to performlookup (NSID→(pre-str, object size)) to obtain pre-str and object sizefor an NSID specified in a received NVMe command. Initiator 1952 can usehardware to calculate objectID number based on LBA # and object size.For example, the system can determine objectID=(LBA #*sector size/objectsize), where a page size can be 4 KB and object size can be 4 MB.Initiator 1952 can use hardware to reformat objectID to a string(obj-str) and concatenate with the prefix string for a namespace toproduce “pre-str.objstr”. A maximum length of an object name string canbe very long for objects using REST gateway for RADOS object (RADOSGW)or object storage service. Block device-based usage (RBD image names)can be shorter.

In some examples, initiator 1952 can support a maximum length, e.g.,256B of object name string. The system can use processor-executedsoftware in SoC 1960 or host to handle the exceptions that exceed thismaximum length.

RDMA QP lookup can include use of hash engine 1954 at least in thefollowing manner. Hash engine 1954 can receive an input (pre-str.objstr,length) and generate hash_value. The hash value can further be updatedby a simple stable “mod” function that is used in Ceph CRUSH algorithm.The system can use a lookup table with input of hash_value %lookup_table_size and output of RDMA QP # or other identifier of a nodeor device (physical or virtual) to send a storage or memory accessrequest to. A memory access request can be a write, a read, a command,status request, and so forth.

Distributed volume manager 1980 can track mapping of LBAs-to-storagenode address for storage or memory nodes that are connected to the hostsystem and participate in storage or memory scale-out. Distributedvolume manager 1980 can program the redirector 1962 with an updated hinttable and/or mapping of LBAs-to-storage node address. Distributed volumemanager 1980 can be implemented in the same server as that of the NIC,same rack as that of the NIC, a POD manager, or a connected device.

FIG. 20 depicts a process to perform hint processing and translate hintsinto an RDMA QP for a target node access. The process can be performedby a redirector and/or initiator. Other types of remote directioncopying can be performed such as by use of iWARP and/or RoCE protocols.At 2002, hints can be received from storage nodes that are remote to aNIC or even local (same rack, server, data center) to a NIC. Hints canindicate information concerning storage nodes that can be used by theNIC to identify a queue pair (QP) for use to convey a command such as anNVMe or NVMe-oF read or write command. A NIC can receive simple hints,striping (striped) hints, or hash hints.

At 2004, a determination is made if the received hint is a hashing hint.If the received hint is not a hashing hint, then 2006 follows. If thereceived hint is a hashing hint, then the process continues to FIG. 21.

At 2006, simple or striping hint process is performed. For a simple orstriping hint, simple or striping hint is translated into a table entrywith RDMA QP number and a hint lookup table is updated at 2008. Simplehint processing can include translating the information in fields(Subsystem NQN, Destination NGUID) tuples into an RDMA QP. A redirectoror other entity can store a mapping table between (Subsystem NQN,Destination NGUID) and RDMA QP. When an RDMA QP is setup with a remotetarget, the corresponding RDMA QP can be added into a lookup table.

Striping hint processing can include simple hint processing and isrepeated for every extent (remote target), the output is a list of RDMAQPs.

On the left side of the process of FIG. 20, at 2010, an applicationexecuting on a host (e.g., running in a virtualized executionenvironment) issues an NVMe command (CMD) to a network interface. At2012, an NVMe-oF initiator processes the CMD. The CMD specifies thenamespace ID (NSID) and an LBA. The NSID and the LBA number are used in2014 for a lookup in the hint lookup table to find a QP identifier basedon the NSID. At 2016, a determination is made if a lookup hit is found.If a lookup hit is found, then the process continues to 2018, where thelooked-up RDMA QP number from a lookup entry is used to perform the CMD.If a lookup hit is not found, at 2020, a default QP number is used orthe command is provided to SoC software for processing. After a QPnumber is identified, the process continues to 2022.

At 2022, the NIC can send an NMVe-oF command to a device associated withan identified RDMA QP number. Protocols other than RDMA can be used suchas iWARP, RoCE, QUIC, TCP or other transport protocols. At 2024, the NICcan wait for CMD completion, e.g., response from remote server with data(read) or completion (write).

FIG. 21 depicts a process to process hashing hints. On the right side,processing of a hashing hint is performed where the hashing hint isreceived from a local or remote storage node using a packet or a remoteredirector that sends a hashing hint using a packet. A software-basedredirector or other hardware and/or software can perform the processingof the right-side of FIG. 21. If the hint is simple or striped, theprocess of FIG. 20 is used. For a hashed hint, the process continues to2102. At 2102, a hash hint log page is processed, the lookup table withthe prefix string (pre-str) for a namespace and object size (objsize) isprogrammed or updated. At 2104, the hash hint is processed to determinethe number of RDMA queue pair (QPs) used for the NSID. At 2106, the hashlookup table is updated or programmed with the mapping between a hashvalue and RDMA QP number. Accordingly, a lookup table and hash tablelookup can be updated using hashing hints.

A left side process is described next and can be used to process areceived command. The left side process of FIG. 21 can be performed by ahardware-based initiator or other hardware and/or software. At 2110, anapplication executing on a host (e.g., running in a virtualizedexecution environment) issues an NVMe command (CMD) to a networkinterface. At 2112, an NVMe-oF initiator processes the CMD and convertsthe command into an NVMe-oF command. CMD specifies the namespace ID(NSID) and specifies an LBA. The NSID is used in a lookup to find prefixstring and object size.

At 2114, the NIC determines the prefix string and object size associatedwith an NSID from a first lookup table whereby an NSID is used to lookupthe prefix string and object size. At 2116, the NIC calculates objectIDbased on LBA number and object size and reformats objectID to a string(pre-str.objstr). For example, reformatting objectID to a string canallow compatibility with Jenkins hashing or other hashing scheme. Insome example, a prefix string is not looked up or used. At 2118, a longstring is input to a hash engine to generate a hash value. For example,a hash engine uses fields (pre-str.objstr) and generates a hash value. Ahash engine (e.g., part of scale-out system and lookup tables) can useJenkins or other hash scheme to calculate a hash value. The hash valuecan further be updated by a simple stable modulo function that is usedin Ceph CRUSH algorithm. At 2120, the hash value is used to find RDMA QPnumber from a second lookup table. At 2122, the NIC can send the NMVe-oFcommand to the device associated with the RDMA QP. Protocols other thanRDMA can be used such as iWARP, RoCE, QUIC, TCP or other transportprotocols. The NIC can wait for CMD completion, e.g., response fromremote server with data (read) or completion (write).

For Storage Performance Development Kit (SPDK), an example of how aredirector applies all the location hints it has accumulated from allthe other redirectors in the NVMe data path is described next. The SPDKimplementation starts by making a simplifying assumption/requirementthat I/Os and hints will align on some bounds (128K, etc.). A Smart NICimplementation can cause I/Os and hints to align on some bounds toenable simplified lookup. The redirector reduces the complete locationhint set to those referring to targets it is currently connected to.These are sorted by starting LBA (low to high) and specificity (largestLBA range to smallest BLA range). Hints referring to the exact same LBArange as the one before them are discarded. This reduced set of hints isthen transformed into what the SPDK implementation calls the rule table.The rule table is an ordered list of LBA, target pairs. Conceptuallythis is produced by considering LBAs in the logical namespace in order,determining which target is indicated by the most specific hint in thereduced list that includes the LBA being considered, and adding an entryto the rule table if this LBA goes to a different target than theprevious LBA. The SPDK implementation walks the reduced hint list inorder, maintaining a stack of hints it has looked past to find morespecific hints. Edges of the location hints are found withoutconsidering each LBA, and omits the rule table as it goes. During anI/O, the SPDK implementation performs a binary search on the IO startLBA in the rule table. The highest entry with an LBA<=the LBA of the IOis the target that will be used.

FIG. 22 depicts a system. The system can use embodiments describedherein at least to use technologies described herein to at least withrespect to management of direct mapping of connected storage or othernodes that storage LBAs and updating of a lookup table of node addressesbased on received hints. System 2200 includes processor 2210, whichprovides processing, operation management, and execution of instructionsfor system 2200. Processor 2210 can include any type of microprocessor,central processing unit (CPU), graphics processing unit (GPU),processing core, or other processing hardware to provide processing forsystem 2200, or a combination of processors. Processor 2210 controls theoverall operation of system 2200, and can be or include, one or moreprogrammable general-purpose or special-purpose microprocessors, digitalsignal processors (DSPs), programmable controllers, application specificintegrated circuits (ASICs), programmable logic devices (PLDs), or thelike, or a combination of such devices.

In one example, system 2200 includes interface 2212 coupled to processor2210, which can represent a higher speed interface or a high throughputinterface for system components that needs higher bandwidth connections,such as memory subsystem 2220 or graphics interface components 2240, oraccelerators 2242. Interface 2212 represents an interface circuit, whichcan be a standalone component or integrated onto a processor die. Wherepresent, graphics interface 2240 interfaces to graphics components forproviding a visual display to a user of system 2200. In one example,graphics interface 2240 can drive a high definition (HD) display thatprovides an output to a user. High definition can refer to a displayhaving a pixel density of approximately 100 PPI (pixels per inch) orgreater and can include formats such as full HD (e.g., 1080p), retinadisplays, 4K (ultra-high definition or UHD), or others. In one example,the display can include a touchscreen display. In one example, graphicsinterface 2240 generates a display based on data stored in memory 2230or based on operations executed by processor 2210 or both. In oneexample, graphics interface 2240 generates a display based on datastored in memory 2230 or based on operations executed by processor 2210or both.

Accelerators 2242 can be a fixed function offload engine that can beaccessed or used by a processor 2210. For example, an accelerator amongaccelerators 2242 can provide compression (DC) capability, cryptographyservices such as public key encryption (PKE), cipher,hash/authentication capabilities, decryption, or other capabilities orservices. In some embodiments, in addition or alternatively, anaccelerator among accelerators 2242 provides field select controllercapabilities as described herein. In some cases, accelerators 2242 canbe integrated into a CPU socket (e.g., a connector to a motherboard orcircuit board that includes a CPU and provides an electrical interfacewith the CPU). For example, accelerators 2242 can include a single ormulti-core processor, graphics processing unit, logical execution unitsingle or multi-level cache, functional units usable to independentlyexecute programs or threads, application specific integrated circuits(ASICs), neural network processors (NNPs), programmable control logic,and programmable processing elements such as field programmable gatearrays (FPGAs). Accelerators 2242 can provide multiple neural networks,CPUs, processor cores, general purpose graphics processing units, orgraphics processing units can be made available for use by artificialintelligence (AI) or machine learning (ML) models. For example, the AImodel can use or include any or a combination of: a reinforcementlearning scheme, Q-learning scheme, deep-Q learning, or AsynchronousAdvantage Actor-Critic (A3C), combinatorial neural network, recurrentcombinatorial neural network, or other AI or ML model. Multiple neuralnetworks, processor cores, or graphics processing units can be madeavailable for use by AI or ML models.

Memory subsystem 2220 represents the main memory of system 2200 andprovides storage for code to be executed by processor 2210, or datavalues to be used in executing a routine. Memory subsystem 2220 caninclude one or more memory devices 2230 such as read-only memory (ROM),flash memory, one or more varieties of random access memory (RAM) suchas DRAM, or other memory devices, or a combination of such devices.Memory 2230 stores and hosts, among other things, operating system (OS)2232 to provide a software platform for execution of instructions insystem 2200. Additionally, applications 2234 can execute on the softwareplatform of OS 2232 from memory 2230. Applications 2234 and OS 2232 canbe executed within a virtual machine environment or containerenvironment with distinct allocated memory regions. Applications 2234represent programs that have their own operational logic to performexecution of one or more functions. Processes 2236 represent agents orroutines that provide auxiliary functions to OS 2232 or one or moreapplications 2234 or a combination. OS 2232, applications 2234, andprocesses 2236 provide software logic to provide functions for system2200. In one example, memory subsystem 2220 includes memory controller2222, which is a memory controller to generate and issue commands tomemory 2230. It will be understood that memory controller 2222 could bea physical part of processor 2210 or a physical part of interface 2212.For example, memory controller 2222 can be an integrated memorycontroller, integrated onto a circuit with processor 2210.

While not specifically illustrated, it will be understood that system2200 can include one or more buses or bus systems between devices, suchas a memory bus, a graphics bus, interface buses, or others. Buses orother signal lines can communicatively or electrically couple componentstogether, or both communicatively and electrically couple thecomponents. Buses can include physical communication lines,point-to-point connections, bridges, adapters, controllers, or othercircuitry or a combination. Buses can include, for example, one or moreof a system bus, a Peripheral Component Interconnect (PCI) bus, a HyperTransport or industry standard architecture (ISA) bus, a small computersystem interface (SCSI) bus, a universal serial bus (USB), or anInstitute of Electrical and Electronics Engineers (IEEE) standard 1394bus (Firewire).

In one example, system 2200 includes interface 2214, which can becoupled to interface 2212. In one example, interface 2214 represents aninterface circuit, which can include standalone components andintegrated circuitry. In one example, multiple user interface componentsor peripheral components, or both, couple to interface 2214. Networkinterface 2250 provides system 2200 the ability to communicate withremote devices (e.g., servers or other computing devices) over one ormore networks. Network interface 2250 can include an Ethernet adapter,wireless interconnection components, cellular network interconnectioncomponents, USB (universal serial bus), or other wired or wirelessstandards-based or proprietary interfaces. Network interface 2250 cantransmit data to a device that is in the same data center or rack or aremote device, which can include sending data stored in memory. Networkinterface 2250 can receive data from a remote device, which can includestoring received data into memory. Various embodiments can be used inconnection with network interface 2250, processor 2210, and memorysubsystem 2220.

In one example, system 2200 includes one or more input/output (I/O)interface(s) 2260. I/O interface 2260 can include one or more interfacecomponents through which a user interacts with system 2200 (e.g., audio,alphanumeric, tactile/touch, or other interfacing). Peripheral interface2270 can include any hardware interface not specifically mentionedabove. Peripherals refer generally to devices that connect dependentlyto system 2200. A dependent connection is one where system 2200 providesthe software platform or hardware platform or both on which operationexecutes, and with which a user interacts.

In one example, system 2200 includes storage subsystem 2280 to storedata in a nonvolatile manner. In one example, in certain systemimplementations, at least certain components of storage 2280 can overlapwith components of memory subsystem 2220. Storage subsystem 2280includes storage device(s) 2284, which can be or include anyconventional medium for storing large amounts of data in a nonvolatilemanner, such as one or more magnetic, solid state, or optical baseddisks, or a combination. Storage 2284 holds code or instructions anddata 2286 in a persistent state (i.e., the value is retained despiteinterruption of power to system 2200). Storage 2284 can be genericallyconsidered to be a “memory,” although memory 2230 is typically theexecuting or operating memory to provide instructions to processor 2210.Whereas storage 2284 is nonvolatile, memory 2230 can include volatilememory (i.e., the value or state of the data is indeterminate if poweris interrupted to system 2200). In one example, storage subsystem 2280includes controller 2282 to interface with storage 2284. In one examplecontroller 2282 is a physical part of interface 2214 or processor 2210or can include circuits or logic in both processor 2210 and interface2214.

A volatile memory is memory whose state (and therefore the data storedin it) is indeterminate if power is interrupted to the device. Dynamicvolatile memory requires refreshing the data stored in the device tomaintain state. One example of dynamic volatile memory includes DRAM(Dynamic Random Access Memory), or some variant such as Synchronous DRAM(SDRAM). A memory subsystem as described herein may be compatible with anumber of memory technologies, such as DDR3 (Double Data Rate version 3,original release by JEDEC (Joint Electronic Device Engineering Council)on Jun. 27, 2007). DDR4 (DDR version 4, initial specification publishedin September 2012 by JEDEC), DDR4E (DDR version 4), LPDDR3 (Low PowerDDR version3, JESD209-3B, August 2013 by JEDEC), LPDDR4) LPDDR version4, JESD209-4, originally published by JEDEC in August 2014), WIO2 (WideInput/output version 2, JESD229-2 originally published by JEDEC inAugust 2014, HBM (High Bandwidth Memory, JESD325, originally publishedby JEDEC in October 2013, LPDDR5 (currently in discussion by JEDEC),HBM2 (HBM version 2), currently in discussion by JEDEC, or others orcombinations of memory technologies, and technologies based onderivatives or extensions of such specifications. The JEDEC standardsare available at www.jedec.org.

A non-volatile memory (NVM) device is a memory whose state isdeterminate even if power is interrupted to the device. In oneembodiment, the NVM device can comprise a block addressable memorydevice, such as NAND technologies, or more specifically, multi-thresholdlevel NAND flash memory (for example, Single-Level Cell (“SLC”),Multi-Level Cell (“MLC”), Quad-Level Cell (“QLC”), Tri-Level Cell(“TLC”), or some other NAND). A NVM device can also comprise abyte-addressable write-in-place three dimensional cross point memorydevice, or other byte addressable write-in-place NVM device (alsoreferred to as persistent memory), such as single or multi-level PhaseChange Memory (PCM) or phase change memory with a switch (PCMS), NVMdevices that use chalcogenide phase change material (for example,chalcogenide glass), resistive memory including metal oxide base, oxygenvacancy base and Conductive Bridge Random Access Memory (CB-RAM),nanowire memory, ferroelectric random access memory (FeRAM, FRAM),magneto resistive random access memory (MRAM) that incorporatesmemristor technology, spin transfer torque (STT)-MRAM, a spintronicmagnetic junction memory based device, a magnetic tunneling junction(MTJ) based device, a DW (Domain Wall) and SOT (Spin Orbit Transfer)based device, a thyristor based memory device, or a combination of anyof the above, or other memory.

A power source (not depicted) provides power to the components of system2200. More specifically, power source typically interfaces to one ormultiple power supplies in system 2200 to provide power to thecomponents of system 2200. In one example, the power supply includes anAC to DC (alternating current to direct current) adapter to plug into awall outlet. Such AC power can be renewable energy (e.g., solar power)power source. In one example, power source includes a DC power source,such as an external AC to DC converter. In one example, power source orpower supply includes wireless charging hardware to charge via proximityto a charging field. In one example, power source can include aninternal battery, alternating current supply, motion-based power supply,solar power supply, or fuel cell source.

In an example, system 2200 can be implemented using interconnectedcompute sleds of processors, memories, storages, network interfaces, andother components. High speed interconnects can be used such as PCIe,Ethernet, or optical interconnects (or a combination thereof).

Embodiments herein may be implemented in various types of computing andnetworking equipment, such as switches, routers, racks, and bladeservers such as those employed in a data center and/or server farmenvironment. The servers used in data centers and server farms comprisearrayed server configurations such as rack-based servers or bladeservers. These servers are interconnected in communication via variousnetwork provisions, such as partitioning sets of servers into Local AreaNetworks (LANs) with appropriate switching and routing facilitiesbetween the LANs to form a private Intranet. For example, cloud hostingfacilities may typically employ large data centers with a multitude ofservers. A blade comprises a separate computing platform that isconfigured to perform server-type functions, that is, a “server on acard.” Accordingly, a blade includes components common to conventionalservers, including a main printed circuit board (main board) providinginternal wiring (i.e., buses) for coupling appropriate integratedcircuits (ICs) and other components mounted to the board.

Various embodiments can be used in data centers to scale-out storage ormemory transactions involving memory pools, storage pools, oraccelerators and using NVMe-oF. Various embodiments can be used by cloudservice providers that use distributed resources (e.g., compute, memory,storage, accelerators, storage). Distributed resources can be locatedamong one or more of: a base station, fog data center, edge data center,or remote data center. Various embodiments can be used in a base stationthat supports communications using wired or wireless protocols (e.g.,3GPP Long Term Evolution (LTE) (4G) or 3GPP 5G), on-premises datacenters, off-premises data centers, edge network elements, fog networkelements, and/or hybrid data centers (e.g., data center that usevirtualization, cloud and software-defined networking to deliverapplication workloads across physical data centers and distributedmulti-cloud environments).

FIG. 23 depicts an example network interface. Transceiver 2302 can becapable of receiving and transmitting packets in conformance with theapplicable protocols such as Ethernet as described in IEEE 802.3,although other protocols may be used, such as but not limited toprotocols described herein. Transceiver 2302 can receive and transmitpackets from and to a network via a network medium (not depicted).Transceiver 2302 can include PHY circuitry 2314 and media access control(MAC) circuitry 2316. PHY circuitry 2314 can include encoding anddecoding circuitry (not shown) to encode and decode data packets. MACcircuitry 2316 can be configured to assemble data to be transmitted intopackets, that include destination and source addresses along withnetwork control information and error detection hash values. Processors2304 can be any processor, core, graphics processing unit (GPU), orother programmable hardware device that allow programming of networkinterface 2300.

Packet allocator 2324 can provide distribution of received packets forprocessing by multiple CPUs or cores using receive side scaling (RSS).Packet allocator 2324 can calculate a hash or make another determinationbased on contents of a received packet to determine which CPU or core isto process a packet.

Interrupt coalesce 2322 can perform interrupt moderation whereby networkinterface interrupt coalesce 2322 waits for multiple packets to arrive,or for a time-out to expire, before generating an interrupt to hostsystem to process received packet(s). Receive Segment Coalescing (RSC)can be performed by network interface 2300 whereby portions of incomingpackets are combined into segments of a packet. Network interface 2300provides this coalesced packet to an application.

Scale out management 2350 can be used to perform embodiments describedherein at least with respect to management of direct mapping of storageor other nodes that storage LBAs and updating of a lookup table of nodeaddresses based on received hints.

Direct memory access (DMA) engine 2352 can copy a packet header, packetpayload, and/or descriptor directly from host memory to the networkinterface or vice versa, instead of copying the packet to anintermediate buffer at the host and then using another copy operationfrom the intermediate buffer to the destination buffer.

Memory 2310 can be any type of volatile or non-volatile memory deviceand can store any queue or instructions used to program networkinterface 2300. Transmit queue 2306 can include data or references todata for transmission by network interface. Receive queue 2308 caninclude data or references to data that was received by networkinterface from a network. Descriptor queues 2320 can include descriptorsthat reference data or packets in transmit queue 2306 or receive queue2308. Bus interface 2312 can provide an interface with host device (notdepicted). For example, bus interface 2312 can be compatible with PCI,PCI Express, PCI-x, Serial ATA, and/or USB compatible interface(although other interconnection standards may be used).

Various examples may be implemented using hardware elements, softwareelements, or a combination of both. In some examples, hardware elementsmay include devices, components, processors, microprocessors, circuits,circuit elements (e.g., transistors, resistors, capacitors, inductors,and so forth), integrated circuits, ASICs, PLDs, DSPs, FPGAs, memoryunits, logic gates, registers, semiconductor device, chips, microchips,chip sets, and so forth. In some examples, software elements may includesoftware components, programs, applications, computer programs,application programs, system programs, machine programs, operatingsystem software, middleware, firmware, software modules, routines,subroutines, functions, methods, procedures, software interfaces, APIs,instruction sets, computing code, computer code, code segments, computercode segments, words, values, symbols, or any combination thereof.Determining whether an example is implemented using hardware elementsand/or software elements may vary in accordance with any number offactors, such as desired computational rate, power levels, heattolerances, processing cycle budget, input data rates, output datarates, memory resources, data bus speeds and other design or performanceconstraints, as desired for a given implementation. It is noted thathardware, firmware and/or software elements may be collectively orindividually referred to herein as “module,” “logic,” “circuit,” or“circuitry.” A processor can be one or more combination of a hardwarestate machine, digital control logic, central processing unit, or anyhardware, firmware and/or software elements.

Some examples may be implemented using or as an article of manufactureor at least one computer-readable medium. A computer-readable medium mayinclude a non-transitory storage medium to store logic. In someexamples, the non-transitory storage medium may include one or moretypes of computer-readable storage media capable of storing electronicdata, including volatile memory or non-volatile memory, removable ornon-removable memory, erasable or non-erasable memory, writeable orre-writeable memory, and so forth. In some examples, the logic mayinclude various software elements, such as software components,programs, applications, computer programs, application programs, systemprograms, machine programs, operating system software, middleware,firmware, software modules, routines, subroutines, functions, methods,procedures, software interfaces, API, instruction sets, computing code,computer code, code segments, computer code segments, words, values,symbols, or any combination thereof.

According to some examples, a computer-readable medium may include anon-transitory storage medium to store or maintain instructions thatwhen executed by a machine, computing device or system, cause themachine, computing device or system to perform methods and/or operationsin accordance with the described examples. The instructions may includeany suitable type of code, such as source code, compiled code,interpreted code, executable code, static code, dynamic code, and thelike. The instructions may be implemented according to a predefinedcomputer language, manner or syntax, for instructing a machine,computing device or system to perform a certain function. Theinstructions may be implemented using any suitable high-level,low-level, object-oriented, visual, compiled and/or interpretedprogramming language.

One or more aspects of at least one example may be implemented byrepresentative instructions stored on at least one machine-readablemedium which represents various logic within the processor, which whenread by a machine, computing device or system causes the machine,computing device or system to fabricate logic to perform the techniquesdescribed herein. Such representations, known as “IP cores” may bestored on a tangible, machine readable medium and supplied to variouscustomers or manufacturing facilities to load into the fabricationmachines that actually make the logic or processor.

The appearances of the phrase “one example” or “an example” are notnecessarily all referring to the same example or embodiment. Any aspectdescribed herein can be combined with any other aspect or similar aspectdescribed herein, regardless of whether the aspects are described withrespect to the same figure or element. Division, omission or inclusionof block functions depicted in the accompanying figures does not inferthat the hardware components, circuits, software and/or elements forimplementing these functions would necessarily be divided, omitted, orincluded in embodiments.

Some examples may be described using the expression “coupled” and“connected” along with their derivatives. These terms are notnecessarily intended as synonyms for each other. For example,descriptions using the terms “connected” and/or “coupled” may indicatethat two or more elements are in direct physical or electrical contactwith each other. The term “coupled,” however, may also mean that two ormore elements are not in direct contact with each other, but yet stillco-operate or interact with each other.

The terms “first,” “second,” and the like, herein do not denote anyorder, quantity, or importance, but rather are used to distinguish oneelement from another. The terms “a” and “an” herein do not denote alimitation of quantity, but rather denote the presence of at least oneof the referenced items. The term “asserted” used herein with referenceto a signal denote a state of the signal, in which the signal is active,and which can be achieved by applying any logic level either logic 0 orlogic 1 to the signal. The terms “follow” or “after” can refer toimmediately following or following after some other event or events.Other sequences of steps may also be performed according to alternativeembodiments. Furthermore, additional steps may be added or removeddepending on the particular applications. Any combination of changes canbe used and one of ordinary skill in the art with the benefit of thisdisclosure would understand the many variations, modifications, andalternative embodiments thereof.

Disjunctive language such as the phrase “at least one of X, Y, or Z,”unless specifically stated otherwise, is otherwise understood within thecontext as used in general to present that an item, term, etc., may beeither X, Y, or Z, or any combination thereof (e.g., X, Y, and/or Z).Thus, such disjunctive language is not generally intended to, and shouldnot, imply that certain embodiments require at least one of X, at leastone of Y, or at least one of Z to each be present. Additionally,conjunctive language such as the phrase “at least one of X, Y, and Z,”unless specifically stated otherwise, should also be understood to meanX, Y, Z, or any combination thereof, including “X, Y, and/or Z.”

Illustrative examples of the devices, systems, and methods disclosedherein are provided below. An embodiment of the devices, systems, andmethods may include any one or more, and any combination of, theexamples described below.

Example 1 includes a network interface comprising: an initiator todetermine a storage node associated with an access command based on anassociation between an address in the access command and a storage nodeand a redirector to update the association between an address in theaccess command and a storage node based on messages from one or moreremote storage nodes.

Example 2 includes any example, wherein the association comprises anamespace identifier corresponding to a prefix string and/or objectsize.

Example 3 includes any example, and includes a table including at leastone association.

Example 4 includes any example, wherein the access command is compatiblewith a protocol employed by NVMe over Fabrics.

Example 5 includes any example, wherein the access command comprises oneor more of the following: a write, a read, a command, and statusrequest.

Example 6 includes any example, wherein the initiator is to determine astring and prefix string for a namespace.

Example 7 includes any example, wherein the initiator is to calculate ahash based on the string.

Example 8 includes any example, wherein the initiator is to determine aremote direct memory access (RDMA) queue-pair (QP) lookup using a tablebased on the hash.

Example 9 includes any example, wherein to update the associationbetween an address in the access command and a storage node based onmessages from one or more remote storage nodes and wherein a messageincludes a hint, the redirector is to: based on detection that the hintis a simple hint or striped hint: update an association between simpleor striping hint with a remote direct memory access (RDMA) queue-pair(QP) number or based on detection that the hint is a hash hint: processa hash hint log, update an association between a prefix string for anamespace and object size, process the hash hint to determine a RDMA QPnumber used for a namespace identifier (NSID), and update an associationbetween a hash value to be with the determined RDMA QP number.

Example 10 includes any example, wherein the initiator is to identify aremote direct memory access (RDMA) queue-pair (QP) for a namespaceidentifier using a lookup and use the identified RDMA QP to transmit acommand to a storage node.

Example 11 includes any example, wherein the initiator is to identify aremote direct memory access (RDMA) queue-pair (QP) for a namespaceidentifier (NSID) using a lookup and if an RDMA QP is not identified foran NSID, a default RDMA QP is used to transmit a command to a storagenode.

Example 12 includes any example, and includes one or more of: a hostdevice, server, rack, or datacenter.

Example 13 includes a method, performed in a network interface,comprising: determining a storage node associated with an access commandbased on an association between an address in the access command and astorage node and updating the association based on messages from one ormore remote storage nodes.

Example 14 includes any example, wherein the association comprises alook-up table associating namespace identifier with prefix string andobject size.

Example 15 includes any example, wherein the access command iscompatible with NVMe over Fabrics.

Example 16 includes any example, wherein the access command comprises awrite, a read, a command, or status request.

Example 17 includes any example and includes calculating a hash based onthe access command and determining a remote direct memory access (RDMA)queue-pair (QP) lookup based on the hash.

Example 18 includes a system comprising: one or more remote storagenodes and a network interface communicatively coupled to one or moreremote storage nodes, the network interface comprising: an initiator todetermine a storage node associated with an access command based on anassociation between an address in the access command and a storage nodeand a redirector to update the association based on messages from one ormore remote storage nodes.

Example 19 includes any example, wherein the association comprises alook-up table to associate a namespace identifier with prefix string andobject size.

Example 20 includes any example, wherein the access command iscompatible with NVMe over Fabrics.

Example 21 includes any example, wherein the initiator is to determine aremote direct memory access (RDMA) queue-pair (QP) lookup using a tablebased on a hash.

What is claimed is:
 1. A network interface comprising: an initiator todetermine a storage node associated with an access command based on anassociation between an address in the access command and a storage nodeand a redirector to update the association between an address in theaccess command and a storage node based on messages from one or moreremote storage nodes.
 2. The network interface of claim 1, wherein theassociation comprises a namespace identifier corresponding to a prefixstring and/or object size.
 3. The network interface of claim 2, furthercomprising a table including at least one association.
 4. The networkinterface of claim 1, wherein the access command is compatible with aprotocol employed by NVMe over Fabrics.
 5. The network interface ofclaim 1, wherein the access command comprises one or more of thefollowing: a write, a read, a command, and status request.
 6. Thenetwork interface of claim 1, wherein the initiator is to determine astring and prefix string for a namespace.
 7. The network interface ofclaim 6, wherein the initiator is to calculate a hash based on thestring.
 8. The network interface of claim 7, wherein the initiator is todetermine a remote direct memory access (RDMA) queue-pair (QP) lookupusing a table based on the hash.
 9. The network interface of claim 1,wherein to update the association between an address in the accesscommand and a storage node based on messages from one or more remotestorage nodes and wherein a message includes a hint, the redirector isto: based on detection that the hint is a simple hint or striped hint:update an association between simple or striping hint with a remotedirect memory access (RDMA) queue-pair (QP) number or based on detectionthat the hint is a hash hint: process a hash hint log, update anassociation between a prefix string for a namespace and object size,process the hash hint to determine a RDMA QP number used for a namespaceidentifier (NSID), and update an association between a hash value to bewith the determined RDMA QP number.
 10. The network interface of claim1, wherein the initiator is to identify a remote direct memory access(RDMA) queue-pair (QP) for a namespace identifier using a lookup and usethe identified RDMA QP to transmit a command to a storage node.
 11. Thenetwork interface of claim 1, wherein the initiator is to identify aremote direct memory access (RDMA) queue-pair (QP) for a namespaceidentifier (NSID) using a lookup and if an RDMA QP is not identified foran NSID, a default RDMA QP is used to transmit a command to a storagenode.
 12. The network interface of claim 1, comprising one or more of: ahost device, server, rack, or datacenter.
 13. A method, performed in anetwork interface, comprising: determining a storage node associatedwith an access command based on an association between an address in theaccess command and a storage node and updating the association based onmessages from one or more remote storage nodes.
 14. The method of claim13, wherein the association comprises a look-up table associatingnamespace identifier with prefix string and object size.
 15. The methodof claim 13, wherein the access command is compatible with NVMe overFabrics.
 16. The method of claim 13, wherein the access commandcomprises a write, a read, a command, or status request.
 17. The methodof claim 13, comprising calculating a hash based on the access commandand determining a remote direct memory access (RDMA) queue-pair (QP)lookup based on the hash.
 18. A system comprising: one or more remotestorage nodes and a network interface communicatively coupled to one ormore remote storage nodes, the network interface comprising: aninitiator to determine a storage node associated with an access commandbased on an association between an address in the access command and astorage node and a redirector to update the association based onmessages from one or more remote storage nodes.
 19. The system of claim18, wherein the association comprises a look-up table to associate anamespace identifier with prefix string and object size.
 20. The systemof claim 18, wherein the access command is compatible with NVMe overFabrics.
 21. The system of claim 18, wherein the initiator is todetermine a remote direct memory access (RDMA) queue-pair (QP) lookupusing a table based on a hash.